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958 results for “Data quality”
DISCOVER-AQ Texas Deployment Texas Commission on Environmental Quality Ground Site Data
DISCOVERAQ_Texas_Ground_TCEQ_Data contains data collected by the Texas Commission on Environmental Quality (TCEQ) at various ground sites around the study area, including Aldine, Channelview, Clinton, Conroe Airport, Deer Park, Galveston, Harris County, LaPorte Airport, Manvel Croix, Seabrook Park, Smith Point, Texas Avenue, UH Coastal Center, UH Liberty, UH Sugarland, and West Houston as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
NARSTO Pacific 2001 Air Quality Study (PAC2001) Greater Vancouver Regional District (GVRD) and and Canadian Air and Precipitation Monitoring Network (CAPMoN) Supplemental Air Quality Data
NARSTO_PAC2001_GVRD_CAPMON_AIR_QUAL_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Pacific 2001 Air Quality Study (PAC2001) Greater Vancouver Regional District (GVRD) and and Canadian Air and Precipitation Monitoring Network (CAPMoN) Supplemental Air Quality Data product. Data was obtained from January 1, 2001 to January 1, 2002. Air quality monitoring data routinely collect by the GVRD CAPMoN during the sampling period of PAC2001, are included as supplemental data for PAC2001.The GVRD monitoring network of 20 sites continued operation during the PAC2001 field study period, with enhanced quality assurance (QA) and quality control (QC) activities. At all sites, meteorological measurements were carried out at a 5-min time resolution. At a few specially equipped sites, particle mass PM10 were measured using tapered element oscillating microbalances (TEOMs). The network data complements the special study sites and form a spatial distribution of the pollutants. CAPMoN is a non-urban air quality monitoring network with siting criteria designed to ensure that the measurement locations are regionally representative (not affected by local sources of air pollution).The objectives were to determine the spatial patterns and establish the temporal trends of pollutants related to acid rain; provide for long-range transport model evaluations and effects research (aquatic, terrestrial, building materials and health); ensure the compatibility of federal, provincial and U.S. measurements; and study atmospheric processes. Scientists involved with the measurement of atmospheric pollution in urban centers would consider most CAPMoN sites to be remote and pristine. There are currently 19 measurement sites in Canada and 1 in the U.S. The Saturna Island site is located in the PAC2001 area of interest. PAC2001 was conducted from 1 August to 31 September 2001 in the Lower Fraser Valley (LFV), British Columbia, Canada. The study consisted of individual research projects organized to address several issues on ambient particulate matter and ozone that are important to policy makers. A special issue of Atmospheric Environment [Vol. 38(34), Nov 2004] described specific study objectives (Li, 2004) and presented a series of results papers from the field study. The ground sampling sites during the study were (1) Cassiar Tunnel, (2) Slocan Park, (3) Langley Ecole Lochiel, (4) Sumas Eagle Ridge, and (5) Golden Ears Provincial Park. Aloft measurements were taken from a Convair 580 and a Cessna 188. Selected measurement data were compiled for each site and aircraft and are archived as site-specific data sets.North American Research Strategy for Tropospheric Ozone (NARSTO), which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.
NARSTO EPA Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Formaldehyde and Hydrogen Peroxide Data
NARSTO_EPA_SS_HOUSTON_TEXAQS2000_PM_SIZE_MASS is North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Houston, Texas Air Quality Study 2000 (TexAQS2000) Formaldehyde and Hydrogen Peroxide Data. It contains continuous formaldehyde (HCHO) and hydrogen peroxide (H2O2) measurements collected in August - September 2000 during TEXAQS2000 at the Houston Regional Monitoring (HRM) Site 3 monitoring station. Integrated single point measurements of 3-minute samples were collected every 10 minutes. The Houston Supersite is one of several Supersites that was established in urban areas within the United States by the U.S. Environmental Protection Agency (EPA) to better understand the measurement, sources, and health effects of suspended particulate matter (PM). The overall goals were to characterize the composition and identify the sources of PM in Southeastern Texas, to develop and test new methods for characterizing fine particulate matter, and to collect data on the physical and chemical characterization of fine PM that can be used to support exposure and health effects studies. NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.
Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11
The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11 consists of three data layers created to provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data. The Data Context raster explains pixels with a "0" population estimate in the population count and density rasters based on information included in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Water Mask raster distinguishes between pixels that are completely water and/or ice (Total Water Pixels), pixels that contain water and land (Partial Water Pixels), pixels that are completely land (Total Land Pixels), and pixels that are completely ocean water (Ocean Pixels). The Mean Administrative Unit Area raster represents the mean input Unit size in square kilometers and provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density rasters are created. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.
Figure 3 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 3. Examples of ways in which aggregators can make uncertainties and biases visually available to users of Primary Biodiversity Data. Such information can be employed to filter data and to quantify and correct for biases in sampling effort, respectively. (a) Georeferenced localities of a given species are simply plotted in geographic space (black dots; current practice). (b) Those same localities appear using symbologies that provide additional information; a hazy cloud indicates the radius of error for localities holding information regarding uncertainty of the georeference, and localities lacking such data appear only as hollow black circles. (c) Information appears that reflects the results of sampling effort, by showing in gray the georeferenced localities for all species belonging to a more inclusive target group (i.e., all species detected with the same techniques as the species of interest; conventions the same as in b). Note that the right-hand side of the study region lacks records for any species of the target group, suggestive of very low sampling effort there.
Figure 2 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 2. Use of individual and collective Stable Unique Identifiers (e.g., DOIs) in biodiversity informatics. (a) Individual Stable Unique Identifier (I-SUI) allows linking diverse data domains for a given organism. In this example, an I-SUI links the voucher specimen and associated Primary Biodiversity Data (e.g., date and locality) of an individual mammal to information regarding various aspects of molecular- to population-level biology. (b) Collective Stable Unique Identifier (C-SUI) denotes a set (i.e., a list) of individual identifiers. For example, a C-SUI could indicate the n individual records used in a given analysis.
Figure 1 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 1. Simplified overview of the interactions and flow of data among providers, aggregators, and users in biodiversity informatics. Numbers indicate the typical order of actions: 1. Aggregator receives data uploads (and periodic updates) from providers; 2. User makes a data query to aggregator's online portal; 3. Aggregator responds to query by making data available on portal (for viewing and/or download). Note that by querying a single aggregator, a user can receive data from multiple providers. Additionally, multiple intermediate aggregators typically exist, feeding into the largest ones most commonly consulted by users (e.g., GBIF).
Air quality during COVID-19 in Beijing_data_sharing
<p>Data for air quality during COVID-19 in Beijing.</p>
Machine Learning-Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy - Supporting Data
<p>Supporting data for the reproduction of the results reported in Corbetta, E., Bocklitz, T., Machine learning based estimation of experimental artifacts and image quality in fluorescence microscopy (2024) [1].</p> <h2><strong>MM-IQA_Images_png and </strong><strong>MM-IQA_Images_tif</strong></h2> <p>Folder containing all the supporting datasets of the publication.</p> <ul> <li>images_manual_inspection: semisynthetic dataset used for the manual inspection of the quality metrics.</li> <li>images_lda_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.</li> <li>images_lda_prediction: datasets predicted by the LDA model. <ul> <li>images_experimental: every subfolder is a dataset composed of measurements of a different sample. Images are from publicly available datasets from [2] and [3].</li> <li>images_known_semisynthetic: knwon semisynthetic dataset used for prediction, assessment and interpretation of the trained model.</li> </ul> </li> </ul> <p>Tif files are the original data used for the study.</p> <h2><strong>MM-IQA_Source_data</strong></h2> <p>Folder containing all the supporting metadata of the publication.</p> <ul> <li>manual_inspection: quality metrics computed for the semisynthetic dataset used for the manual inspection. <ul> <li>manual_inspection_bg: indices for the selection of the background region in each sample.</li> <li>manual_inspection_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>manual_inspection_metrics: quality metrics computed for the dataset, used for the manual inspection.</li> <li>manual_inspection_samples: free parameters associated to each image of the dataset for manual inspection.</li> </ul> </li> <li>LDA_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model. <ul> <li>lda_metrics_synthetic+semisynthetic_uniform_max: quality metrics computed for the training dataset, with maximum normalization of the images. (Not used in the manuscript)</li> <li>lda_metrics_synthetic+semisynthetic_uniform_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>parameters_all_degradations: parameters used for the generation of the simulated artifacts.</li> </ul> </li> <li>LDA_prediction: datasets predicted by the LDA model. <ul> <li>experimental: quality metrics computed for measurements of different samples. Images are from publicly available datasets from [2] and [3].</li> <li>known_semisynthetic: metadata for the knwon semi-synthetic dataset used for prediction, assessment and interpretation of the trained model: <ul> <li>known_semisynthetic_free_parameters: parameters used for the generation of the simulated artifacts.</li> <li>known_semisynthetic_metrics_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.</li> <li>known_semisynthetic_maxnorm_lda_results: lda prediction results, when metrics are maximum normalized to the training dataset.</li> <li>known_semisynthetic_znorm_lda_results: lda prediction results, when metrics are z-score normalized to the training dataset.</li> </ul> </li> </ul> </li> </ul> <p>Source data can be used to reproduce the results of the manuscript, using the codes shared in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <h3><em>How to use the source data</em></h3> <p>The following table describes which data can be used in the scripts provided in the public GitLab repository <em><a href="https://git.photonicdata.science/elena.corbetta/multi-marker-iqa" target="_blank" rel="noopener">multi-marker-IQA</a>.</em></p> <table> <tbody> <tr> <td><strong>Script</strong></td> <td><strong>Data to use</strong></td> <td><strong>Details</strong></td> </tr> <tr> <td>01_quality_metrics</td> <td>Subfolders of MM-IQA_Images_png</td> <td>Include all the images to evaluate in a single subfolder in <code>/test_images</code></td> </tr> <tr> <td> </td> <td>background_idx.xlsx</td> <td>The indices for the samples to evaluate must be included in the table</td> </tr> <tr> <td> <p>01_quality_metrics_visualization</p> <p>01_quality_metrics_visualization_notebook</p> </td> <td>manual_inspection_metrics.xlsx</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td> </td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td> </td> </tr> <tr> <td> <p>02_lda_training+prediction</p> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> <tr> <td> </td> <td>known_semisynthetic_metrics_rescale01</td> <td>As prediction dataset</td> </tr> <tr> <td> </td> <td>Every metadata included in LDA_predcition/experimental/</td> <td>As prediction dataset</td> </tr> <tr> <td> <p>02_lda_visualization</p> <p>02_lda_visualization_notebook</p> </td> <td>known_semisynthetic_maxnorm_lda_results</td> <td> </td> </tr> <tr> <td> </td> <td>known_semisynthetic_znorm_lda_results</td> <td> </td> </tr> <tr> <td>Notebook_test-iqa</td> <td>A small dataset with image data and the relative background index, if available.</td> <td>Use a limited number of images.</td> </tr> <tr> <td>Notebook_mm-iqa_workflow</td> <td>Any image dataset with the relative background indices</td> <td>For quality assessment and as prediction dataset</td> </tr> <tr> <td> </td> <td>lda_metrics_synthetic+semisynthetic_uniform_rescale01</td> <td>As training dataset</td> </tr> </tbody> </table> <p> </p> <h2>MM-IQA_Scripts</h2> <ul> <li><strong>multi-marker-iqa-main</strong>: original GitLab repository for MM-IQA, version available at the date of manuscript publication.</li> <li><strong>Notebooks_peer_review</strong>: additional notebooks generated during the peer-review process with the computation of metrics for natural images and correlation measures.</li> </ul>
Camera trap data accompanying paper titled: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size"
<p>ZIp file that contains two camera trap datasets that support the experiments of the paper title: "Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size". The paper is currently under submission.</p>
Variability of RRate: breathing and quality data
<p><span><i>Background:</i> Respiratory rate is difficult to measure, especially in neonates who have an irregular breathing pattern. The World Health Organisation recommends a one minute count, but there is limited data to support this length of observation. We sought to evaluate agreement between the respiratory rate (RR) derived from capnography in neonates, over 15 seconds, 30 seconds, 120 seconds and 300 seconds, against the recommended 60 seconds rate.</span></p> <p><span><i>Methods:</i> Neonates at two hospitals in Nairobi were recruited and had capnograph waveforms recorded using the Masimo Rad 97. A single high quality 5 minute epoch was randomly chosen from each subject. For each selected epoch, the mean RR was calculated using a breath-detection algorithm applied to the waveform. The RR in the first 60 seconds was compared to the mean RR measured over the first 15 seconds, 30 seconds, 120 seconds, full 300 seconds, and last 60 seconds. We calculated bias and limits of agreement for each comparison and used Bland-Altman plots for visual comparisons.</span></p> <p><span><i>Results:</i> A total of 306 capnographs were analysed from individual subjects. The subjects had a median gestation age of 39 weeks with slightly more females (52.3%) than males (47.7%). The majority of the population were term neonates (70.1%) with 39 (12.8%) having a primary respiratory pathology. There was poor agreement between all the comparisons based on the limits of agreement [confidence interval], ranging between 11.9 [-6.79 to 6.23] breaths per minute in the one versus two minutes comparison, and 34.7 [-17.59 to 20.53] breaths per minute in the first versus last minute comparison. Worsening agreement was observed in plots with higher RRs.</span></p> <p><span><i>Conclusions:</i> Neonates have high variability of RR, even over a short period of time. A slight degradation in the agreement is noted over periods shorter than one minute. However, this is smaller than observations done 3 minutes apart in the same subject. Longer periods of observation also reduce agreement. For device developers, precise synchronization is needed when comparing devices to reduce the impact of RR variation. For clinicians, where possible, continuous or repeated monitoring of neonates would be preferable to one time RR measurements. </span></p>
AI-assisted Quality Control Study of Multimodal Data in the Epidemiological Survey of Shanghai Nicheng Cohort Study
ClinicalTrials.gov study NCT06961461. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Collection of Biological and Environmental Samples and Clinical Data From Anonymous Adult Men and Women for Quality Control and Methods Development and Evaluation (ASCA)
ClinicalTrials.gov study NCT02940015. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Suitability of Some Data Quality Controls Thresholds for Genetic Association Studies of Admixed Population
ClinicalTrials.gov study NCT02770001. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Systematic evaluation of RNA quality and microarray data reliability in formalin-fixed paraffin-embedded and fresh frozen tissue samples
GEO Series GSE104568. Homo sapiens; Rattus norvegicus. 15 samples. Type: Expression profiling by array.
Variability of RRate: breathing and quality data
Open the record for dataset details and reuse information.
Data Exploration, Quality Control and Statistical Analysis of ChIP-exo/nexus Experiments
GEO Series GSE84830. Escherichia coli. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
MMS 4 Electron Drift Instrument (EDI) Quality 0 Counts, Level 2 (L2), Burst Mode, 7.8125 ms Data
Electron Drift Instrument (EDI) Q0 Burst Survey, Level 2, 0.0078125 s Data (128 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 2 Electron Drift Instrument (EDI) Quality 0 Counts, Level 2 (L2), Burst Mode, 7.8125 ms Data
Electron Drift Instrument (EDI) Q0 Burst Survey, Level 2, 0.0078125 s Data (128 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 3 Electron Drift Instrument (EDI) Quality 0 Counts, Level 2 (L2), Burst Mode, 7.8125 ms Data
Electron Drift Instrument (EDI) Q0 Burst Survey, Level 2, 0.0078125 s Data (128 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
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.