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232 results for “time measurement”
Time series of carbon dioxide and methane fluxes measured with eddy covariance for Falling Creek Reservoir in southwestern Virginia, USA during 2020-2025
We measured carbon dioxide and methane flux exchange with the atmosphere at the deepest site of Falling Creek Reservoir (Vinton, Virginia, USA) every 30 minutes from 04 April 2020 to 31 December 2025. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA) as a primary drinking water source. The dataset consists of micrometeorological and flux data collected using an eddy covariance system (LiCor Biosciences, Lincoln, Nebraska, USA) and analyzed with associated Eddy Pro software (Eddy Pro Version 7.0.6), including carbon dioxide, methane, and water vapor. All analysis scripts are included for data processing and quality assurance/quality control following best practices.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Time series of carbon dioxide fluxes measured with eddy covariance for Danjiangkou Reservoir in Hubei Province, China during 2022-2024
This dataset contains half-hourly micrometeorological and eddy covariance flux measurements of carbon dioxide (CO₂) collected over the water surface of the Danjiangkou Reservoir in Hubei Province, China, from April 2022 to November 2024. The eddy covariance tower was installed at the deepest point of the reservoir, which serves as a critical water source for water supply and regional ecological functions in the middle reaches of the Yangtze River. Measurements were obtained using a LI-COR eddy covariance system (LI-COR Biosciences, Lincoln, NE, USA), and fluxes were calculated using EddyPro software (version 7.0.6). The dataset includes CO₂ and CH₄ fluxes as well as supporting micrometeorological, radiation, and water temperature measurements. All data were processed following established best practices for eddy covariance measurements, including comprehensive quality assurance and quality control procedures, which are fully documented and included with the dataset.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
Time series of optical measurements (absorbance, fluorescence) for Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir in southwestern Virginia, USA 2019-2025
Depth profiles and surface dissolved samples analyzed for optical analyses (absorbance, fluorescence) were sampled from 2019 to 2025 in three drinking water reservoirs located in southwestern Virginia, USA including Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), and Falling Creek Reservoir (Vinton, Virginia). The reservoirs are owned and operated by the Western Virginia Water Authority as either primary or secondary drinking water sources for Roanoke, Virginia. The dataset consists of depth profiles at the deepest site of each reservoir, surface water samples from reservoir tributaries, and additional samples at within-reservoir sites. In Beaverdam and Falling Creek Reservoir, we collected depth profiles, gauged weir, and wetland samples approximately fortnightly throughout the summer stratified period (June 2019 - November 2019) and surface samples approximately monthly from May 2019 to October 2019 and in March 2020. Beaverdam Reservoir depth samples were additionally collected in summer 2022. We collected depth profiles and tributary samples monthly to seasonally in Carvins Cove Reservoir from late 2021 - 2023. From May 2024 - April 2025, we sampled depth profiles at multiple transects across the reservoir and surface water samples in one tributary approximately monthly at Carvins Cove. Absorbance was measured as colored dissolved organic matter (CDOM) using a spectrophotometer. Fluorescence was measured as fluorescent dissolved organic matter (fDOM) using a spectrofluorometer as excitation emission matrices (EEMs). Absorbance and fluorescence results are reported along with PARAFAC model results applied to the collected EEMs samples. Data visualization and quality assurance/quality control (QA/QC) scripts accompany the data package.
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Reconfiguration Time Measurements for Evolving Hardware
<p>The dataset contains the raw measurements of the time necessary to reconfigure Lattice iCE40 FPGAs.</p> <p>The main goal is to see the effect of two approaches of implementing the reconfiguration process in software for Evolvable Hardware. One approach is to use existing open-source software externally, the other is to integrate the functionality in the software for Evolvable Hardware.</p> <p>The conditions for the reconfiguration measurements were altered in four dimensions:</p> <ol> <li>The FPGA board <ul> <li>iCEstick <ul> <li>Lattice iCE40 HX1K FPGA</li> <li>Sample bitstream created by <a href="https://github.com/evolvablehardware/BitstreamEvolution">Bitstream Evolution Software</a></li> <li>No direct reconfiguration possible</li> </ul> </li> <li>iCE40 breakout board (ICE40HX8K-B-EVN) <ul> <li>Lattice iCE40 HX8K FPGA</li> <li>Sample bitstream created by CoBEA</li> </ul> </li> </ul> </li> <li>Write Target <ul> <li>Flash <ul> <li>Bitstream first written to an external EEPROM, then pulled by the FPGA from the EEPROM</li> </ul> </li> <li>Direct <ul> <li>Bitstream written directly to the FPGA</li> </ul> </li> </ul> </li> <li>Reconfiguration approach <ul> <li>Icestorm <ul> <li>Use external tools from Project Icestorm</li> </ul> </li> <li>CoBEA <ul> <li>Use integrated function of CoBEA reconfiguration module</li> </ul> </li> </ul> </li> <li>Bitstream size <ul> <li>Full <ul> <li>No size reduction</li> </ul> </li> <li>Compact <ul> <li>Size reduced as much as allowed by write approach <ul> <li>Icestorm: skip BRAM</li> <li>CoBEA: skip BRAM, skip comment, full compaction (level 4)</li> </ul> </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>This upload contains three groups of files:</p> <ul> <li>2 bitstreams <ul> <li>One for each used FPGA board</li> </ul> </li> <li>12 measurements <ul> <li>Comma-separated values</li> <li>File name contains the contiditions of the measurement</li> <li>Only 12 out of 16 possible combinations of conditions as "iCEstick" cannot be combined with "Direct"</li> </ul> </li> <li>1 aggregated statistic <ul> <li>Comma-separated values</li> <li>Minimum, mean, maximum, mean-minimum, maximum-mean for every measurement</li> </ul> </li> </ul>
Multivariate Time Series data of Fatigued and Non-Fatigued Running from Inertial Measurement Units
<p>The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.<br><br>The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here are segmented strides from the two 400m runs of each of the 19 participants. The labels on the data represent the participant number and whether it was a fatigued stride ('F') or a not fatigued stride ('NF').<br>The data used from the sensors includes data from the accelerometer in three directions (X, Y, Z) and the gyroscope in three directions (X, Y, Z). The direction of each of the axis is relative to the sensor. Two extra signals, magnitude acceleration and magnitude gyroscope were derived from the component signals and included in the analysis.</p><p>Kindly cite one of the following papers when using this data:</p><p>B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612</p><p>B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a></p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Time series of high-frequency sensors measuring water temperature and dissolved oxygen at discrete depths in Falling Creek Reservoir, Virginia, USA in 2012-2018
We measured water temperature and dissolved oxygen at multiple depths in Falling Creek Reservoir (Vinton, Virginia, USA) with high-frequency (10 to 15-minute) sensors for different durations during 2012 to 2018. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. All measurements were collected at discrete depths at the deepest site of the reservoir adjacent to the dam. The sensors consisted of: 1) InsiteIG dissolved oxygen and water temperature sensors (Model 20 dissolved oxygen sensor) at both 1 m (November 2015 - December 2018) and 8 m (September 2012 - December 2018) and 2) HOBO (HOBO Pendant Temperature/Light 64K Data Logger) water temperature loggers deployed at 1, 2, 3, 4, 5, 6, 7, 8, and 9.3 m depths (September 2015 - January 2018).
Short term accretion measured using marker horizon of feldspar at Space For Time locations.
This dataset provides a survey of short term accretion across the PIE LTER at both high and low marsh sites. A marker horizon plot was established and sampled annually to determine depth of accretion since deployment.
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Data for "An autonomous quantum machine to measure the thermodynamic arrow of time"
<p>Numerical simulation data from the article "An autonomous quantum machine to measure the thermodynamic arrow of time"</p> <p>J. Monsel, C. Elouard, A. Auffèves <em>npj Quantum Inf</em> <strong>4</strong>, 59 (2018). <a href="https://doi.org/10.1038/s41534-018-0109-8" target="_blank" rel="noopener">https://doi.org/10.1038/s41534-018-0109-8</a></p> <p>See the jupyter notebook for the data analysis and figures.</p> <p>The code to perform the numerical simulations is given in the repository <a href="https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system" target="_blank" rel="noopener">https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system</a>.</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019)
<p>Rates of N<sub>2</sub> fixation were measured using the <sup>15</sup>N<sub>2</sub> 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 <sup>15</sup>N<sub>2</sub> gas was first dissolved into seawater and 100 mL of the resulting <sup>15</sup>N<sub>2</sub>-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of <sup>15</sup>N<sub>2</sub>-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the <sup>15</sup>N<sub>2</sub> 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). </p>
iCE40 Bitstream Size Reduction - Bitstreams and Reconfiguration Time Measurements
<p>The HDF5 file contains data from an experiment concerning bitstream size reduction for Lattice iCE40 FPGAs.</p> <p>Five projects were synthesized with two different toolchains and afterwards compacted and compressed. Two compaction levels were applied to each of the ten original bistreams, resulting in a total of 30 bitstreams. The ten original bitstreams were also compressed with two different compression tools, resulting in 20 compressed versions.</p> <p>The reconfiguration time for each of the 30 bitstreams was measured 10.000 times.</p> <p>Toolchains:</p> <ul> <li>Lattice iCEcube2</li> <li>Open-source toolchain (Yosys, nextpnr, Project IceStorm)</li> </ul> <p>Projects:</p> <ul> <li>blinky: A simple example design that is included in the open-source toolchain.</li> <li>ehw: An evolved design.</li> <li>attosoc: A minimal RISC-V system on a chip, that is used for tests in the open-source toolchain.</li> <li>updater: A design that receives a new configuration, decrypts it (AES) and writes it to flash.</li> <li>picosoc: RISC-V system on a chip</li> </ul> <p>Compaction levels:</p> <ul> <li>original: Uncompacted bitstream</li> <li>builtin: Compacted with two methods also available in iCEcube2</li> <li>compact: Compacted with all five methods</li> </ul> <p>Compression tools:</p> <ul> <li>icecompr: Included in Project IceStorm</li> <li>gzip: Version 1.9, compression level --best</li> </ul> <p> </p> <p>File structure:</p> <p>The data is organzied hierarchical. It is first split between bitstreams, measurements and compressed versions. Bitstreams and measurements then are divided by toolchain, then by project and finally by compaction level. The compressed versions are divided by toolchain, then by project and finally by compression tool.</p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
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>
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.