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232 results for “time measurement”
Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology
<p>This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a></p> <p><br> For a complete description of our approach for axonal morphology estimation in-vivo, see: <em>Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023.</em></p> <p>This dataset contains the following Matlab files:</p> <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> <p>The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a><br> <br> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.</p> <p> </p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p> <p> </p>
Pulse Profiles and Times of Arrival Measurements from a Rotating Radio Transient Census with the Irish LOFAR station
<p>The reduced data produced as a part of a census of rotating radio transients (RRATs) with the Irish LOFAR station.</p> <p> </p> <p>This deposit contains:</p> <ul> <li>Metadata regarding observed data</li> <li>A copy of RFI-zapped, single pulse archives</li> <li>A copy of the time-flattened periodic emission archives</li> <li>A copy of the measured pulse times of arrival</li> <li>Ephemerides used and produced as a part of the work</li> </ul> <p>Additional data can be made available on request to the author.</p>
Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019) (Reformatted)
<p>Rates of N2 fixation were measured using the 15N2 isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles. The 15N2 gas was first dissolved into seawater and 100 mL of the resulting 15N2-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of 15N2-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the 15N2 tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). Dataset has been reformatted to meet submission requirements for Simons CMAP.</p>
First time-resolved measurement of infrared scintillation light in gaseous xenon
<p>Repository with supplemental data to:<br> <strong>First time-resolved measurement of infrared scintillation light in gaseous xenon</strong>. Piotter, M., Cichon, D., <em>Hammann, R.</em>, Jörg, F., Hötzsch, L.,<em> Marrodán Undagoitia, T. Eur. Phys. J. C</em> <strong>83</strong>, 482 (2023).<br> A pre-print of the article is available <em>on arXiv: </em><a href="https://arxiv.org/abs/2303.09344">2303.09344</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p> </p> <p>The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods:</p> <ul> <li><strong>waveform_before.csv, waveform_during.csv, waveform_after.csv</strong>: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> <li><strong>light_yield_ir.csv: </strong>This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.</li> <li><strong>waveform_495.csv, waveform_742.csv, waveform_1047.csv:</strong> These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column <em>dt</em> is given in nanoseconds relative to the UV pulse and <em>counts </em>corresponds to counts per nanosecond per 100 UV events.</li> </ul> <p> </p> <p><strong>Code examples for plotting the data:</strong></p> <p>The following Python code reproduces figure 9 in the publication:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': df = pd.read_csv("light_yield_ir.csv") color_pairs = [("#365898", "#B7D0FF"), ("#AB123B", "#F0B5C5"), ("#E1992E", "#F1DAB9")] fig, ax = plt.subplots(1, figsize=(4, 3)) for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"], color_pairs): # Plot systematic error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"], ls="", elinewidth=3, capsize=0, ecolor=cs[1] ) # Plot estimator with statistical error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"], ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1, marker=".", label=fit_func_str) # Cosmetics ax.set_xlabel("Pressure [mbar]") ax.set_ylabel("IR light yield [ph / MeV]") ax.set_ylim(1200, 12_500) ax.legend(frameon=False, loc="upper left") plt.show()</code></pre> <p> </p> <p>The IR time response of figure 8 can be redrawn as follows:</p> <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': fig, ax = plt.subplots(1, figsize=(4, 3)) for label in ["before", "during", "after"]: df = pd.read_csv(f"waveform_{label}.csv") ax.step(df["dt"], df["counts"], label=label) # Cosmetics ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]") ax.set_ylabel("Counts per 1 ns per 100 UV events") ax.legend(frameon=False, loc="upper right") plt.show()</code></pre> <p> </p>
University of Kansas Field Station: Forest demography, 1980 – 2015. On ten study plots established on three management units all live trees with a dbh > 7.5 cm (3 in) were identified to species, measured, and tagged. Trees were initially measured in 1980/1981 and re-measured in three successive time periods: 1993/95; 2002/03; and 2014/15. Trees will be measured again in 2025/26.
In 1980 researchers at the University of Kansas initiated a long-term experiment monitoring the composition of oak-hickory forest communities at the University’s field station near Lawrence, Kansas. The purpose of the study was to determine how forest species composition varied temporally across distinct habitats that varied in topography, elevation, sun exposure, management history and successional stage. Ten permanent sites were sampled approximately each decade with data collection periods of 1980/81, 1993/95, 2002/03, and 2014/15. Trees with a minimum diameter at breast height (dbh) of ≥ 7.5 cm were tagged, identified to species and measured. Trees will be measured again in 2025/26.
Nutrient and stoichiometric time series measurements of decomposing coarse detritus in freshwaters worldwide from literature published between 1976-2020.
This data publication is a database of published estimates of nitrogen, phosphorus, and carbon content of decomposing coarse detritus though time in freshwater ecosystems worldwide. Nutrient content measurements are paired with estimates of detrital mass loss within decomposition time series (i.e., defined cohorts of decomposing material through time) with the goal of understanding patterns and drivers of temporal elemental dynamics of freshwater detritus. A systematic literature search for aquatic decomposition experiments conducted on 29 April 2020 generated 580 records (after trimming for duplicates and obvious relevance). From this literature pool, we extracted 810 decomposition time series and associated environmental data (e.g., temperature, water quality, detrital characteristics). Time series included in this synthesis include a range of detritus types (including terrestrial and aquatic plant material, carcasses, dung, and veneers), ecosystems (including streams, lakes, rivers, and wetlands), and settings (including natural ecosystems, field mesocosms, and laboratory microcosms).
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Demographic measures of Liatris ohlingerae (Asteraceae) in 20 populations across multiple habitats and time-since-fire intervals in south central Florida from 1997-2017
Demographic data were collected on 2,858 tagged individually marked plants annually from 1997 to 2017 in 20 populations across three sites on the southern end of the Lake Wales Ridge in south central Florida, USA. Our goal was to understand demographic responses including recruitment, survival, reproduction and mortality of individuals across populations, habitat types and fire-return-intervals. Habitats include rosemary scrub, scrubby flatwoods and human created sandy roadsides. Populations spanned three sites including Archbold Biological Station (18 populations), Florida Forest Service Arbuckle Tract of the Lake Wales Ridge State Forest (1 population), and Florida Fish and Wildlife Conservation Commission Gould Road property (1 population). Annual demographic measures include survival (including plant dormancy), stage, measures of size, reproductive effort and herbivory. Plots were surveyed annually during flowering in August for previously marked plants and searched for newly recruited putative seedlings or previously missed larger adults. This landscape is managed with periodic prescribed fire impacting some populations with additional post-burn censuses completed after the burn. In addition, damage following three hurricanes in 2004 were recorded. Plants were followed through their lifecycle and after four years of no aboveground growth, plants were assumed dead and tags removed from the field.
Final chlorophyll and temperature measurements at 10m depth, offshore of Dana Point, California as part of an Ocean Institute time series, 2006 - 2025.
Time series of chlorophyll and temperature at 10m offshore of Dana Point, California. Measurements made as part of education/outreach programs involving students and teachers in oceanographic sampling and analytic sensitivity to time series. Cruises are conducted twice, monthly (or other) where sampling is performed by students assisted by technicians and supervisors.
PIE LTER time series of methane, CO2 and N2O ebullition measurements at four headwater streams in Massachusetts and New Hampshire.
Methane ebullition was monitored at four headwater streams during 2018 and 2019. Stationary bubble traps were deployed from approximately May through October. CC and SB were monitored in 2018 and 2019, while DB and CB were only monitored in 2019. 12 traps were deployed at CC, SB, and DB, and 9 traps were deployed at CB. The concentration measured in the emitted gas was multiplied by the volume measured in a trap to calculated the total methane flux via ebullition. The traps were visited at least once weekly. The mean, median, minimum, and maximum rate of ebullition across all traps at a site over a two week period are listed here. Relevant publications: Robison, A.L. (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. Robison, A.L., W.M. Wollheim, B. Turek, C. Bova, C Snay, & R.K. Varner (in review). Spatial and temporal heterogeneity of methane ebullition in lowland headwater streams. Limnology and Oceanography.
PIE LTER herbivory measurement associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains aggregated observations of predation and herbivory on tethered bait in each quadrat of sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. Measurements consist of the consumption status of tethered squid or kelp pieces as a measure of energy transfer between trophic levels. Pieces were left in the field for five days and observers recorded the status of bait over time as either entirely missing, partially consumed, having scrape marks, or fully intact. These measurements can be used to calculate consumption rates (i.e. energy transfer) over time. Notes include fields discussing any additional observations - e.g., if a stick was found missing.
Real-time measurement and source apportionment of elements in Delhi's atmosphere
<p>Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
Picosecond time-resolved antibunching measures nanoscale exciton motion, annihilation, and true number of chromophores
<p>The particle-like nature of light becomes evident in the photon statistics of fluorescence of single quantum systems as photon antibunching. In multichromophoric systems, exciton diffusion and subsequent annihilation occur. These processes also yield photon antibunching but cannot be interpreted reliably. Here, we develop picosecond time-resolved antibunching (psTRAB) to identify and decode such processes. We use psTRAB to measure the true number of chromophores on well-defined multichromophoric DNA-origami structures, and precisely determine the distance-dependent rates of annihilation between excitons. Further, psTRAB allows us to measure exciton diffusion in mesoscopic H- and J-type conjugated-polymer aggregates. We distinguish between one-dimensional intra-chain and three-dimensional inter-chain exciton diffusion at different times after excitation and determine the disorder-dependent diffusion lengths. Our method provides a new lens through which excitons can be studied at the single-particle level, enabling the rational design of improved excitonic probes such as ultra-bright fluorescent nanoparticles, and materials for optoelectronic devices. Here we demonstrate the raw data of DNA Origami Microscopy on which our findings based on. </p>
A versatile microfluidic platform measures hyphal interactions between Fusarium graminearum and Clonostachys rosea in real-time
<p>Routinely, fungal-fungal interactions (FFIs) are studied on agar surfaces. However, this format restricts high-resolution dynamic imaging. To gain experimental access to FFIs at the hyphal level in real-time, we developed a microfluidic platform, a FFI device. This device utilises microchannel geometry to enhance the visibility of hyphal growth and provides control channels to allow comparisons between localised and systemic effects. We demonstrate its function by investigating the FFI between the biological control agent (BCA) <em>Clonostachys rosea </em>and the plant pathogen <em>Fusarium graminearum. </em>Microscope image analyses confirm the inhibitory effect of the necrotrophic BCA and we show that a loss of fluorescence in parasitised hyphae of GFP-tagged <em>F. graminearum </em>coincides with the detection of GFP in mycelium of <em>C. rosea</em>. The versatility of our device to operate under both water-saturated and nutrient-rich as well as dry and nutrient-deficient conditions, coupled with its spatio-temporal output, opens new opportunities to study relationships between fungi.</p>
Supplementary Data for: A time-calibrated 'Tree of Life' of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world
<p>This compendium of files includes the dated phylogenetic tree in Newick format (<strong>Data S1</strong>), the list of statistical routines used for the three empirical case studies (<strong>Data S2</strong>), and the high-resolution version of the figures in the supplementary materials and main text (<strong>Data S3</strong>) for the <em>Earth-Science Reviews</em> paper "A time-calibrated ‘Tree of Life’ of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world", which is under consideration. The best-scoring molecular tree (<strong>Data S1</strong>) can be opened using freely available programs like R (R Development Core Team, 2021), Dendroscope (Huson and Scornavacca, 2012), and FigTree (Rambaut, 2018).</p> <p>Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es OR Jorge.Garcia-Giron@oulu.fi) if you face any trouble downloading, opening, or using these files.</p> <ul> <li>Huson, D. H., & Scornavacca, C. (2012). Dendroscope 3: An interactive tool for rooted phylogenetic trees and networks. <em>Systematic Biology</em>, <em>61(6)</em>, 1061–1067.</li> <li>R Development Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/</li> <li>Rambaut, A. (2018). FigTree. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. http://tree.bio.ed.ac.uk/software/figtree/</li> </ul>
Photon time-of-flight histograms measured with a photon-counting diffuse LiDAR on Crook Glacier and Collier Glacier, Oregon
<p>This data set contains photon time-of-flight histograms measured in September 2021 on Crook Glacier, Oregon, and two sites on Collier Glacier, Oregon (USA). Each data file is associated with a single measurement using a photon-counting diffuse LiDAR. The files contain a header with geo-location (WGS84) and instrument settings as well as the raw count numbers and integration time for each temporal bin. The given arrival times represent the center of each temporal bin. The color naming scheme of the file names represents the used laser wavelength (blue=405nm, green=520nm, red=640nm), the last number in each filename represents the distance between laser and detector (i.e. 1.8m at 520nm for file "green5_1.8.txt").</p> <p>The data is organized in folders for each site plus an additional folder containing Matlab-code needed for data evaluation. The code uses this folder structure for relative path referencing. Data is evaluated using ExampleDataEvalV2.m, which employs the other three files as helper functions. The helper function ReadTofHisto.m reads the raw data from the measurement files and provides a named structure with the header information.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus ALLGAIER, Matthew G. COOPER, Anders E. CARLSON, Sarah W. COOLEY, Jonathan C. RYAN, Brian J. SMITH, "Direct measurement of optical properties of glacier ice using a photon-counting diffuse LiDAR", in preparation (2022)</p>
Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space).
<p>Data set from Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space), and figures.</p>
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
Proxies' response times measured by clients in an emulated community network
<p>13 virtual nodes were deployed in Planetlab testbed (https://www.planet-lab.org) to emulate a small community network with 8 clients and 5 proxies. Each client probed all proxies every 10 seconds during two days. The same file (http://ovh.net/files/1Mb.dat) was requested in all probes. A probe was considered successful if the file was completely downloaded by the client. In this case, the response time was registered by the client, considering the time elapsed from the moment the client sent the request until the last byte of the response was received.</p> <p>This dataset contains the proxies' response times that were measured by clients in sucessful probes.</p>
Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures
<p>These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.</p> <p>The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.</p> <p>The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:</p> <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>
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.