Introduction

Intensive care has played a pivotal role during the COVID-19 pandemic as many patients developed severe pulmonary complications. The availability of information in pediatric intensive care (PICUs) remains limited. The purpose of this study is to characterize COVID-19 positive admissions (CPAs) in the United States and to determine factors that may impact those admissions.

Materials and Methods

This is a retrospective cohort study using data from the COVID-19 dashboard virtual pediatric system) containing information regarding respiratory support and comorbidities for all CPAs between March and April 2020. The state level data contained 13 different factors from population density, comorbid conditions and social distancing score. The absolute CPAs count was converted to frequency using the state’s population. Univariate and multivariate regression analyses were performed to assess the association between CPAs frequency and endpoints.

Results

A total of 205 CPAs were reported by 167 PICUs across 48 states. The estimated CPAs frequency was 2.8 per million children. A total of 3,235 tests were conducted with 6.3% positive tests. Children above 11 years of age comprised 69.7% of the total cohort and 35.1% had moderated or severe comorbidities. The median duration of a CPA was 4.9 days [1.25-12.00 days]. Out of the 1,132 total CPA days, 592 [52.2%] were for mechanical ventilation. The inpatient mortalities were 3 [1.4%]. Multivariate analyses demonstrated an association between CPAs with greater population density [beta-coefficient 0.01, p<0.01] and increased percent of children receiving the influenza vaccination [beta-coefficient 0.17, p=0.01].

Conclusions

Inpatient mortality during PICU CPAs is relatively low at 1.4%. CPA frequency seems to be impacted by population density while characteristics of illness severity appear to be associated with ultraviolet index, temperature, and comorbidities such as Type 1 diabetes. These factors should be included in future studies using patient-level data.

Methods This study utilized only publicly available, deidentified, state-level data. As such, no institutional review board review or approval was sought.

Endpoint identification and data collection

The following data was identified for collection regarding the CPAs themselves: number, duration, need for various ventilatory support measures, severity of comorbidities, and the total number of COVID-19 tests conducted. The following data was collected regarding US states: pediatric population, state population (pediatric and adult) density, air and drinking water quality, average temperature, average ultraviolet index, prevalence of pediatric obesity, type 1 diabetes mellitus (DM) and asthma, the proportion of children who smoke cigarettes, received the influenza vaccine, had health insurance, and received home health care, race, percent of households with children below the poverty line, highest education level of adults in homes with children, and the social distancing score by global positional satellite data (Supplementary Table 1).

The data regarding the CPAs themselves was collected from the publicly available COVID-19 dashboard provided by the Virtual Pediatric System (VPS), which collects data from several PICUs in the US. COVID-19 data was collected from March 14th through April 14th, 2020, in order to represent one full month of data. Data regarding number of centers, number of tests, and number of CPAs was captured in absolute counts. Data regarding CPAs duration was collected in days. The respiratory support modalities for which data was available were room air (RA), nasal cannula (NC) and for the advanced respiratory support modalities (i.e. other than RA and NC) there was available data for high flow nasal cannula (HFNC), non-invasive positive pressure ventilation (NIPPV), conventional mechanical ventilation (MCV), high frequency oscillatory ventilation (HFOV), and extracorporeal membrane oxygenation (ECMO), and was captured in duration (days) of their use. Data regarding severity of comorbidities is reported in the VPS dashboard and the percentage of CPAs with moderate or severe degree of comorbidities was collected.

State-wide data for the analyses were collected from a variety of sources with the complete list of sources provided as Supplementary Material 1. Children’s population data and pediatric comorbidity data was obtained from 2018, as these were the most recent and comprehensive data available. The sources for these other data points were generally US government-based efforts to capture state-level data on various medical issues, however, not all states reported data for all the endpoints (Supplementary Table 2).

Endpoints were assigned to the authors for collection. One author was responsible for collecting data for each state for the variables assigned. Once these data were collected a different author, who did not primarily collect data for that specific endpoint, verified the numbers for accuracy. Finally, values in the top and bottom 10th percentile were identified and verified by a third author.

Statistical analyses

As the data was collected for each state and intended for state-level analyses, and each state has a different pediatric population, the absolute numbers of CPAs for each state were not directly comparable. Thus, the absolute CPAs count for each state was first converted to a frequency of CPAs per 1,000,000 children using the specific state’s population. This CPAs frequency was then used as the dependent variable in a series of single-independent variable linear regressions to determine the univariate association between CPAs frequency and the other endpoints. Multivariate regression was conducted with CPAs frequency as the dependent variable and with other variables entered as independent variables. Forward stepwise regression was utilized with the model with greatest R-squared value being used for the analyses.

Next, a composite endpoint called “percent of PICUs days requiring advanced respiratory support” was created. This consisted of the total duration of HFNC, NIPPV, MCV, HFOV, and ECMO divided by the total PICUs admission duration. This was then modeled similarly to CPAs frequency. Next, a composite outcome called “percent of PICU days requiring intubation” was created. This consisted of the total duration of MCV and HFOV divided by the total PICU admission duration. This, too, was then modeled similarly as CPA frequency. Lastly, an endpoint called “PICUs duration per admission” was created for each state and consisted of the total CPAs PICUs duration for that specific state divided by the number of CPAs reported by that state. This was also then modeled similarly to CPA frequency.

All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0. A p-value of 0.05 was considered statistically significant. All statistical analyses were done at the state-level with state-level data. Analyses were not conducted at a patient-level with patient-level data. Any use of the word significant here-on in the manuscript refers to “statistically significant” unless explicitly specified otherwise.

• Data Set Overview
• =================

This Data Set consists of 4 s edited, Wave Electric Field Intensities from the Voyager 2 Plasma Wave Receiver Spectrum Analyzer obtained in the Vicinity of the Uranian Magnetosphere. For each 4 s Interval, a Field Strength is determined for each of the sixteen Spectrum Analyzer Channels whose Center Frequencies range from 10 Hz to 56.2 kHz and which are logarithmically spaced in Frequency, four Channels per Decade. The Time associated with each Set of Intensities (sixteen Channels) is the Time of the Beginning of the Scan. During Data Gaps where complete 4 s Spectra are missing, no Entries exist in the File, that is, the Gaps are not Zero-filled or Tagged in any other way. When one or more Channels are missing within a Scan, the missing Measurements are Zero-filled. Data are edited but not calibrated. The Data Numbers in this Data Set can be plotted in raw Form for Event Searches and simple Trend Analysis since they are roughly proportional to the Log of the Electric Field Strength. Calibration Procedures and Tables are provided for use with this Data Set; the use of these is described below.

• Use of Voyager PWS Calibration Tables
• =====================================

The Voyager PWS Calibration Tables are given in two plain ASCII Text Files named VG1PWSCL.TAB and VG2PWSCL.TAB (for Voyager 1 and Voyager 2, respectively). These provide Information to convert the uncalibrated 'Data Number' Output of the PWS sixteen-channel Spectrum Analyzer to calibrated Antenna Voltages for each Frequency Channel. Following is a brief Description of these Files and a Tutorial in their Application.

The first Column lists an uncalibrated Data Number followed by the corresponding Value in calibrated Volts for each of the sixteen Frequency Channels of the PWS Spectrum Analyzer. Each Line contains Calibrations for successive Data Number Values ranging from 0 through 255. (Data Number 0 actually represents the Lack of Data since the Baseline Noise Values for each Channel are all above that.)

A Data Analysis Program may load the appropriate Table into a Data Structure and thus provide a simple Look-up Scheme to obtain the appropriate Voltage for a given Data Number and Frequency Channel. For example, the following VAX FORTRAN Code may be used to load a Calibration Array for Voyager 2 PWS:

• real*4 cal(16,0:255)
• open(unit=10,file='vg2pwscl.tab'status='old' *
• do i=0,38
• read(10,*) ! Skip 38 Lines of Label Info
• end do *
• do i=0,255
• read(10,*) idn,(cal(ichan,i),ichan=1,16)
• end do *
• close(10)

Then, given an uncalibrated Data Value idn for the Frequency Channel ichan, the corresponding calibrated Antenna Voltage would be given by the following Array Reference:

• volts=cal(ichan,idn)

This may be converted to a Wave Electric Field Amplitude by dividing by the effective Antenna Length in meters, 7.07 m. That is:

• efield=cal(ichan,idn)/7.07

Spectral Density Units may be obtained by dividing the Square of the Electric Field Value by the nominal Frequency Bandwidth of the corresponding Spectrum Analyzer Channel.

• specdens=(cal(ichan,idn)/7.07)^2/bandwidth(ichan)

Finally, Power Flux may be obtained by dividing the Spectral Density by the Impedance of Free Space in Ohms:

• pwrflux=((cal(ichan,idn)/7.07)^2/bandwidth(ichan))/376.73

Of course, for a particular Application, it may be more efficient to apply the above Conversions to the Calibration Table directly.

The Center Frequencies and Bandwidths of each PWS Spectrum Analyzer Channel for the Voyager 2 PWS are given below:

+----------------------------------------+

| Channel | Center Frequency | Bandwidth |

| 1 | 10.0 Hz | 2.16 Hz | | 2 | 17.8 Hz | 3.58 Hz | | 3 | 31.1 Hz | 4.50 Hz | | 4 | 56.2 Hz | 10.7 Hz | | 5 | 100 Hz | 13.8 Hz | | 6 | 178 Hz | 28.8 Hz | | 7 | 311 Hz | 39.8 Hz | | 8 | 562 Hz | 75.9 Hz | | 9 | 1.00 kHz | 75.9 Hz | | 10 | 1.78 kHz | 151 Hz | | 11 | 3.11 kHz | 324 Hz | | 12 | 5.62 kHz | 513 Hz | | 13 | 10.0 kHz | 832 Hz | | 14 | 17.8 kHz | 1260 Hz | | 15 | 31.1 kHz | 2400 Hz | | 16 | 56.2 kHz | 3800 Hz | +----------------------------------------+

A Failure in the Voyager 2 Flight Data System which occurred about three Months after Launch has adversely affected the Calibration of PWS Channels 9 through 16. An Algorithm has been devised to partially correct for this Failure, and has proven useful for Voyager 2 Jupiter, Saturn, and Uranus Encounters, but is not valid for Earth-Jupiter Cruise and may be modified in the Future. The following Implementation of this Correction Algorithm in VAX FORTRAN assumes that uncalibrated Data Numbers are stored in a sixteen-element Integer Array, idn, with the Array Index equal to the PWS Channel Number:

• real*4 tonl(9:16) ! The Old Noise Level for v2
• Data tonl/2.0,1.0,-1.0,-2.0,-3.0,1.0,2.0,1.0/ *
• do ichan=9,16
• if(idn(ichan).gt.0) then
• if(idn(ichan).lt.64) idn(ichan)=64
• if(idn(ichan).le.72) then
• idn(ichan)=int(tonl(ichan)-530.4+8.6*real(idn(ichan)))
• else
• idn(ichan)=int(tonl(ichan)+20.113+0.99*real(idn(ichan)))
• end if
• end if
• end do

This Correction should not be applied permanently to the Voyager 2 Calibration Table since it is valid for a limited Time Span and may be modified in the Future.

Additional Information about this Data Set and the Instrument which produced it can be found elsewhere in this Catalog. An Overview of the Data in this Data Set can be found in (Gurnett et al., 1986) and a complete Instrument Description can be found in (Scarf and Gurnett, 1977).

+-------------------------------------------------------+

| Parameter Characteristics | Value |

| Sampling Parameter Name | TIME | | Sampling Parameter Resolution | 4.000000 | | Minimum Sampling Parameter | 197708201553.000000 | | Maximum Sampling Parameter | N/A | | Sampling Parameter Interval | 4.000000 | | Minimum Available Sampling Int | 4.000000 | | Sampling Parameter Unit | SECOND | | Data Set Parameter Name | PLASMA WAVE SPECTRUM | | Noise Level | 0.000005 | | Data Set Parameter Unit | VOLT/METER | +-------------------------------------------------------+

Overview: Novel analysis methods were applied to processed climate model data, including based on outputs previously made available from the Energy Sector for Climate Information (ESCI) project (e.g., ISBN: 978-1-925738-32-2; https://www.climatechangeinaustralia.gov.au/en/projects/esci/). The analysis methods applied here include using expanded ensembles that combine projections per degree warming from different emissions pathways and time periods, thereby increasing sample size for enhanced confidence in results. The resultant analysis data are referred to here as Expanded Ensemble Analysis (EEA) data, provided for mean and extreme values of four variables: daily maximum temperature (tasmax), daily minimum temperature (tasmin), daily precipitation (pr) and the Forest Fire Danger Index (FFDI). Further details on methods, data, uncertainties and guidance for interpretation are in the journal paper doi:10.3389/fclim.2024.1492228 and references therein.

Analysis data details: The EEA data files contain values for the modelled change per degree (Celsius; C) global warming, with individual files provided for the 10th percentile, 90th percentile and mean of the ensemble (also noting potential for changes outside of the 10th to 90th percentile range). Those data are provided for locations where the modelled change is significant at a statistical confidence level above 90%, with a value of -999999 used to fill other regions. For rainfall, some inland regions are also filled with -999999 due to uncertainties associated with very sparse observations data used for the bias correction (estimated here based on where annual average rainfall is less than 140 mm per year during the historical baseline period 1980 to 2005). Data are also provided for the historical baseline period (1980-2005) that these changes are calculated with respect to. Analysis was also done for other time periods during the period of available data from 1980 to 2099, including for comparison with observations-based analysis data for past decades. Data for extremes include analysis of occurrence frequencies for exceeding fixed magnitude thresholds (e.g., tasmax > 40°C, 'gt_40C'; tasmin < 0°C, 'lt_0C'; pr > 50 mm, 'gt_50mm'; FFDI > 50, 'gt_50'), as well as for percentile-based thresholds such as values that may be expected to be exceeded only once per year on average (1-yr average recurrence interval: ARI) at a given location during the historical baseline period (i.e., the occurrence frequency of values greater than the 1-yr ARI: 'gt_1yr_ARI'). Data files are provided here in NetCDF format with zip compression.

Input data: The input data used for the analysis methods applied here include processed climate model data previously made available from the ESCI project (which included bias corrected data from all available dynamical downscaling approaches throughout Australia, e.g., ISBN: 978-1-925738-32-2; https://www.climatechangeinaustralia.gov.au/en/projects/esci/), with further details documented in the journal paper that accompanies this dataset (doi:10.3389/fclim.2024.1492228). The ESCI projections data used a bias correction method known as Quantile Matching for Extremes (QME), trained using analysis data based on observations on a 0.05-degree (approximately 5 km) latitude and longitude grid throughout Australian land locations, with documentation on QME available (ISBN: 978-1-925738-75-9) and Python code adaptation https://github.com/AusClimateService/QME. The analysis data provided here are on a 0.05-degree grid, smoothed with a ±0.25-degree moving average in latitude and longitude. The model data are based on the CMIP5 set of projections including outputs that were available from three different dynamical downscaling approaches applied for the Australian region, for a moderate (RCP4.5) and high (RCP8.5) emissions pathway, noting potential for similar methods to be applied to bias corrected CMIP6 projections and downscaling for the Australian region as data may become available in the future.

General guidance: Aspects of this analysis were selected somewhat arbitrarily, as well as due to the availability of data, noting that similar analysis could also be done using other selections (such as other thresholds for extremes, other time periods for the baseline, etc.). The study detailing these climate analysis methods (doi:10.3389/fclim.2024.1492228) considered a range of different metrics including some details for climate analogues (e.g., finding different locations that have similar past, current or future climate characteristics to each other) and metrics such as the Probability Ratio (e.g., based on the occurrence frequency of extremes in a current climate period compared to a past climate period with less anthropogenic climate change), noting that about 1.1°C of anthropogenic global warming has already occurred. Although data are not directly provided for such metrics, various aspects relating to these types of metrics can be calculated from the data provided here. It is also noted that a wide range of climate projections data and analyses are available including based on outputs from various state and federal agencies in Australia and globally, with benefits often obtained through considering a broad range of lines of evidence. For example, confidence assessment for projected changes can often benefit from drawing on the combination of multiple datasets of observations and modelling as well as physical process understanding (e.g., ISBN:978-1-925738-32-2; doi:10.5194/hess-28-1251-2024; www.publish.csiro.au/mo/pdf/es15008). None of the authors give any representation or warranty of any kind in relation to the information (including data) provided from this page (as well as from pages connecting to this page for this site), including in relation to the currency, completeness, quality, accuracy or suitability for any purpose of this information. You should consider obtaining expert advice before relying on or using this information in any way.

Funding: This research received support from the ARC Centre of Excellence for Climate Extremes (CLEX), as well as the Melbourne Energy Institute (MEI), through the University of Melbourne.

The results of the very-low frequency (16.7MHz) survey of discrete sources made with the UTR-2 radio telescope is presented. The survey concerns the declination zones -13{deg} to -2{deg}, 0{deg} to 20{deg}, and 41{deg} to 60{deg}. The UTR-2 radio source catalogue contains an estimate of the coordinates and flux densities of 1819 sources measured at a number of the lowest frequencies used in contemporary radio astronomy within the range from 10 to 25 MHz. The catalogue is made of two parts: mean.dat: the averaged values of the coordinates and the corresponding errors, the source flux-density at the middle UTR-2 frequency 16.7 MHz as obtained from measured spectrum of the source at all UTR-2 frequencies and its error, the value of measured low-frequency spectral index with respect to which the estimate of has been obtained, the parameter W characterizing the integral reliability of the obtained source estimates and the corresponding object name from higher-frequency radio survey provided the source has been identified. To indicate the integral readability of the source parameters obtained we used the symbols A, B and C. These reliability estimates take into account the total number of measurements, coordinate scatter, number of frequencies and hour-angle settings at which the source parameters have been evaluated. The highly reliable observation results have been marked with symbol A . The sources whose parameters can be used without an additional analysis are marked with B and sources whose parameters are to be used with care have been marked with C. data.dat: this file contains the experimental estimates of the source coordinates and flux densities as well as their errors at each operating frequency of the UTR-2 in the order of their increasing; the total number (N) of successive observations according to which the estimates were obtained and the number of different hour-angle settings (NRA) of the reception pattern at which the source was observed. In cases when the observations did not allow us to obtain a reliable estimates of a source flux density the catalogue contains only their upper limits which are not accompanied by errors. The approximated values of as well as low-frequency spectral indices are presented only for those sources which have flux density estimates obtained at not less than three different UTR-2 frequencies. Cone search capability for table VIII/41/mean (Averaged values and cross-identifications) Cone search capability for table VIII/41/data (Experimental estimates at each operating frequency of the UTR-2)

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

Corresponding Dataset of Advanced Water Vapor
Radiometer Data for Juno Gravity Science
Troposphere Calibrations
README FILE
Dustin Buccino
August 24, 2021
Jet Propulsion Laboratory
California Institute of Technology

=============================================================================
INTRODUCTION
=============================================================================

This dataset contains high rate data collected by the Advanced Water
Vapor Radiometer (AWVR) at the Deep Space Network's Goldstone Complex in
California. This dataset is provided in order to supplement the submitted
article to the "Radio Science" journal

Buccino, D.R., et al (2021), Performance of Earth Troposphere
Calibration Measurements with the Advanced Water Vapor Radiometer
for the Juno Gravity Science Investigation, Radio Science, submitted
October 2021.

******************************************************************
* ANY USERS OF JUNO GRAVITY SCIENCE DATA ARE HIGHLY ENCOURAGED *
* TO INSTEAD REFER TO THE OFFICIAL ARCHIVE ON THE NASA PLANETARY *
* DATA SYSTEM. THIS SUPPLEMENTAL DATA SET DOES NOT CONTAIN ANY *
* GRAVITY SCIENCE DATA; IT ONLY CONTAINS HIGHER RATE AWVR DATA *
******************************************************************

Additional Juno Gravity Science Data may be found at the Planetary Data
System:

Buccino, D. R. (2016). Juno jupiter gravity science raw data set
V1.0, JUNO-J-RSS-1 JUGR-V1.0, NASA planetary data system (PDS).
Retrieved from https://atmos.nmsu.edu/PDS/data/jnogrv_1001/

=============================================================================
ARCHIVE INFORMATION
=============================================================================

This archive contains several data types, located within subdirectories.

ROOT
`- PJ03/

This directory contains all PJ-03 related data, including
path delay, path delay rate, calibration values, and frequency
residuals.

`- PJ06/

This directory contains all PJ-06 related data, including
path delay, path delay rate, and calibration values.

`- PJ08/

This directory contains all PJ-08 related data, including
path delay, path delay rate, and calibration values.

`- ADEV/

This directory contains troposphere scintillation Allan deviations
from each perijove. Files are named using the start time of the file,
in YYYYMMDDHHMM format, where YYYY is the year, MM is the month,
DD is the day of month, HH is the hour, and MM is the minute.

`- STATS/

This directory contains the Juno perijove frequency residual
statistics. Only one file is present in this directory.

=============================================================================
FILE FORMAT
=============================================================================

This dataset contains two separate file formats as described below.
ASCII plain-text files are given with the "*.txt" extension and the
comma-separated text files are given with the "*.csv" extension.

TXT FILES
-------------------------------------------------------------------------

The ASCII plain-text files are human-readable, space-delimited
text files. Each column is defined by a header row which provides
a description of each column. Additional comments may be optionally
specified by starting a row with the character "#".

CSV FILES
-------------------------------------------------------------------------

The Comma-Separated Value (CSV) files are plain-text files. Values in
each data file are separated using a comma ",". Each column is defined
by a header row which provides a description of each column.

=============================================================================
FIGURE REPRODUCTION
=============================================================================

This section will describe the data that are used to produce the figures
in the article that describes this dataset.

FIGURE 1
-------------------------------------------------------------------------

Figure 1 is a photograph and is not included in this dataset.

FIGURE 2
-------------------------------------------------------------------------

Figure 2 is produced using files within the "PJ03", "PJ06", and "PJ08"
directories.

The first row of subfigures are produced by plotting the final three
columns of "pjXX_bt_zenith.txt" as a function of time.

The second row of subfigures are produced by plotting the path delay
componets as a function of time from the "pjXX_pd_awvr.txt" and
"pjXX_pd_tsac.txt" data files.

FIGURE 3
-------------------------------------------------------------------------

Figure 3 is produced using files within the "PJ03", "PJ06", and "PJ08"
directories.

The first row of subfigures are produced by plotting the last column
of "pjXX_freq_awvr.txt" and "pjXX_freq_tsac.txt".

The second row of subfigures are produced by differencing the values.

FIGURE 4
-------------------------------------------------------------------------

Figure 4 is produced using files within the "ADEV" directory.

Each individual file within the "ADEV" directory contains the Allan
deviation. Each Allan deviation is plotted on a log-log scale and is
color-mapped to the calendar date. The file naming convention gives
the calendar date of data collection, with the filenames starting with
YYYYMMDD, where YYYY is 4-digit year, MM is 2-digit month, and DD is
2-digit day of month in UTC time.

FIGURE 5
-------------------------------------------------------------------------

Figure 5 is produced using files within the "PJ03" directory. The
frequency residual from the "pj03_resid_awvr.csv" and
"pj03_resid_tsac.csv" is simply plotted as a function of time.

FIGURE 6
-------------------------------------------------------------------------

Figure 6 is produced using files within the "STATS" directory. This
directory contains a single file, "AWVR_stats_jul2021_v3.csv" and
contains the root-mean-square of the frequency residuals from Juno
perijove passes. The root-mean-square of the frequency residuals
are plotted using a bar plot and the percent improvement is plotted
with a scatterplot.

=============================================================================
ACKNOWLEDGMENTS
=============================================================================

This work was carried out at the Jet Propulsion Laboratory,
California Institute of Technology, under contract with the National
Aeronautics and Space Administration. Government sponsorship acknowledged.

=============================================================================
PRIMARY POINT OF CONTACT
=============================================================================

Dustin Buccino
Jet Propulsion Laboratory
Planetary Radar and Radio Sciences
(818) 393 - 1072
Dustin.R.Buccino@jpl.nasa.gov

=============================================================================
ACRONYMS AND ABBREVIATIONS
=============================================================================

ASCII American Standard Code for Information Interchange
DOY Day of year
DSN Deep Space Network
JPL Jet Propulsion Laboratory
NAIF Navigation Ancillary Information Facility
NASA National Aeronautics and Space Administration
PDS Planetary Data System
RS Radio Science
RSS Radio Science Subsystem
SIS Software Interface Specification
TXT Text file
UTC Universal Time, Coordinated

Five minute ADCP data from ARSV Laurence M. Gould and RVIB Nathaniel B. Palmer cruises to the Southern Ocean from 2001-2003 as part of the Southern Ocean GLOBEC project. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=In 2001, the RVIB NB Palmer and RVIB LM Gould were upgraded to collect a full suite of data needed for full single-ping editing (i.e. processing single pings to ocean velocities). LMG0103, LMG0104, and LMG0106 were collected before the new system was implemented. The single ping data from these cruises can only be used as an aid for looking at amplitude (backscatter). NBP0103, NBP0104, NBP0202, NBP0204, LMG0201A, LMG0203, LMG0205, and LMG0302 datasets contain the complete set of data needed for processing the single ping data. awards_0_award_nid=54694 awards_0_award_number=ANT-9910102 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=9910102 awards_0_funder_name=NSF Antarctic Sciences awards_0_funding_acronym=NSF ANT awards_0_funding_source_nid=369 awards_0_program_manager=Professor Kelly Falkner awards_0_program_manager_nid=51477 cdm_data_type=Other comment=ADCP_5min data from the Southern Ocearn. L. Padman, PI Displayed by /data8/pleiades/rgroman/Scripts/adcp_so_level0.pl, V1.07/24Feb10 Displayed by /data8/pleiades/rgroman/Scripts/adcp_so_level1.pl, V1.05/February ... Displayed by /data8/pleiades/rgroman/Scripts/adcp_so_level2.pl, V1.05/February ... u_m and v_m are the velocity components, in meters. Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.2344.1 Easternmost_Easting=-58.7089 geospatial_lat_max=-52.3528 geospatial_lat_min=-70.6349 geospatial_lat_units=degrees_north geospatial_lon_max=-58.7089 geospatial_lon_min=-77.7755 geospatial_lon_units=degrees_east geospatial_vertical_max=498.0 geospatial_vertical_min=26.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/2344 institution=BCO-DMO instruments_0_acronym=ADCP instruments_0_dataset_instrument_description=Acoustic Doppler Current Profiler, encompasses an array of band widths and frequencies instruments_0_dataset_instrument_nid=4158 instruments_0_description=The ADCP measures water currents with sound, using a principle of sound waves called the Doppler effect. A sound wave has a higher frequency, or pitch, when it moves to you than when it moves away. You hear the Doppler effect in action when a car speeds past with a characteristic building of sound that fades when the car passes. The ADCP works by transmitting "pings" of sound at a constant frequency into the water. (The pings are so highly pitched that humans and even dolphins can't hear them.) As the sound waves travel, they ricochet off particles suspended in the moving water, and reflect back to the instrument. Due to the Doppler effect, sound waves bounced back from a particle moving away from the profiler have a slightly lowered frequency when they return. Particles moving toward the instrument send back higher frequency waves. The difference in frequency between the waves the profiler sends out and the waves it receives is called the Doppler shift. The instrument uses this shift to calculate how fast the particle and the water around it are moving. Sound waves that hit particles far from the profiler take longer to come back than waves that strike close by. By measuring the time it takes for the waves to bounce back and the Doppler shift, the profiler can measure current speed at many different depths with each series of pings. (More from WHOI instruments listing). instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/115/ instruments_0_instrument_name=Acoustic Doppler Current Profiler instruments_0_instrument_nid=405 instruments_0_supplied_name=Acoustic Doppler Current Profiler metadata_source=https://www.bco-dmo.org/api/dataset/2344 Northernmost_Northing=-52.3528 param_mapping={'2344': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/2344/parameters people_0_affiliation=Earth and Space Research Institute people_0_person_name=Dr Susan L. Howard people_0_person_nid=50501 people_0_role=Co-Principal Investigator people_0_role_type=originator people_1_affiliation=Earth and Space Research Institute people_1_person_name=Dr Laurence Padman people_1_person_nid=50500 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Ms Dicky Allison people_2_person_nid=50382 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=SOGLOBEC projects_0_acronym=SOGLOBEC projects_0_description=The fundamental objectives of United States Global Ocean Ecosystems Dynamics (U.S. GLOBEC) Program are dependent upon the cooperation of scientists from several disciplines. Physicists, biologists, and chemists must make use of data collected during U.S. GLOBEC field programs to further our understanding of the interplay of physics, biology, and chemistry. Our objectives require quantitative analysis of interdisciplinary data sets and, therefore, data must be exchanged between researchers. To extract the full scientific value, data must be made available to the scientific community on a timely basis. projects_0_geolocation=Southern Ocean projects_0_name=U.S. GLOBEC Southern Ocean projects_0_project_nid=2039 projects_0_project_website=http://www.ccpo.odu.edu/Research/globec_menu.html projects_0_start_date=2001-01 sourceUrl=(local files) Southernmost_Northing=-70.6349 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-77.7755 xml_source=osprey2erddap.update_xml() v1.3

• Data Set Overview
• =================

This Data Set consists of Electric Field Spectrum Analyzer Data from the Voyager 2 Plasma Wave Subsystem obtained during the entire Mission. Data after 2013-12-31 will be added to the Archive on subsequent Volumes. The Data Set encompasses all Spectrum Analyzer Observations obtained in the Cruise Mission Phases before, between, and after the Jupiter and Saturn Encounter Phases as well as those obtained during the two Encounter Phases.

The Voyager 2 Spacecraft travels from Earth to beyond 100 AU over the Course of this Data Set. To provide some Guidance on when some Key Events occurred during the Mission, the following Table is provided.

+----------------------------------------------------------------+

| Date | Event |

| 1977-08-20 | Launch | | 1979-07-02 | First inbound Bow Shock Crossing at Jupiter | | 1979-08-03 | Last outbound Bow Shock Crossing at Jupiter | | 1981-08-24 | First inbound Bow Shock Crossing at Saturn | | 1981-08-31 | Last outbound Bow Shock Crossing at Saturn | | 1982-04-26 | 10 AU | | 1983-08-30 | Onset of first major LF Heliospheric Radio Event | | 1986-01-24 | First inbound Bow Shock Crossing at Uranus | | 1986-01-29 | Last outbound Bow Shock Crossing at Uranus | | 1986-05-26 | 20 AU | | 1989-08-07 | 30 AU | | 1989-08-24 | First inbound Bow Shock Crossing at Neptune | | 1989-08-28 | Last outbound Bow Shock Crossing at Neptune | | 1992-07-06 | Onset of second major LF Heliospheric Radio Event | | 1993-05-08 | 40 AU | | 1996-10-10 | 50 AU | | 2000-01-27 | 60 AU | | 2002-11-01 | Onset of third major LF Heliospheric Radio Event | | 2003-04-21 | 70 AU | | 2006-07-01 | 80 AU | | 2009-09-03 | 90 AU | | 2012-11-04 | 100 AU | +----------------------------------------------------------------+

• Data Sampling
• =============

This Data Set consists of Full Resolution edited, Wave Electric Field Intensities from the Voyager 2 Plasma Wave Receiver Spectrum Analyzer obtained during the entire Mission. For each Time Interval, a Field Strength is determined for each of the sixteen Spectrum Analyzer Channels whose Center Frequencies range from 10 Hz to 56.2 kHz and which are logarithmically spaced in Frequency, four Channels per Decade. The Time associated with each Set of Intensities (sixteen Channels) is the Time of the Beginning of the Scan. The Time between Spectra in this Data Set vary by Telemetry Mode and range from 4 s to 96 s. During Data Gaps where complete Spectra are missing, no Entries exist in the File, that is, the Gaps are not Zero-filled or Tagged in any other way. When one or more Channels are missing within a Scan, the missing Measurements are Zero-filled. Data are edited but not calibrated. The Data Numbers in this Data Set can be plotted in raw Form for Event searches and simple Trend Analysis since they are roughly proportional to the Log of the Electric Field Strength. Calibration Procedures and Tables are provided for use with this Data Set; the use of these is described below.

For the Cruise Data Sets, the Timing of Samples is dependent upon the Spacecraft Telemetry Mode. In principle, one can determine the Temporal Resolution between Spectra simply by noting the Difference in Time between two Records in the Files. In some Studies, more precise Timing Information is necessary. Here, we describe the Timing of the Samples for the PWS Low Rate Data as a Function of Telemetry Mode.

The PWS Instrument uses two Logarithmic Compressors as Detectors for the sixteen Channel Spectrum Analyzer, one for the bottom (lower frequency) eight Channels, and one for the upper (higher frequency) eight Channels. For each Bank of eight Channels, the Compressor sequentially steps from the lowest frequency of the eight to the highest in a regular Time Step to obtain a complete Spectrum. At each Time Step, the higher frequency Channel is sampled 0.125 s prior to the lower frequency Channel so that the Channels are sampled in the following order with Channel 1 being the lowest frequency Channel (10 Hz) and Channel 16 being the highest (56.2 kHz): 9, 1, 10, 2, 11, 3, ..., 15, 7, 16, 8. The primary Difference between the various Data Modes is the Stepping Rate from one Channel to the next (ranging from 0.5 s to 12 s, corresponding to the Temporal Resolutions between complete Spectra of 4 s to 96 s).

In the following Table, we present the Hexadecimal ID for the various Telemetry Modes, the Mode Mnemonic ID, the Time between Frequency Steps, and the Time between complete Spectra. We also provide the Offset from the Beginning of the Instrument Cycle (one complete Spectrum) identified as the Time elapsed from the Time Tag of each Record to the Time of the Sampling for the first high-frequency Channel (Channel 9) and for the first low-frequency Channel (Channel 1).

+--------------------------------------------------------------------------------------------------------------------------+

| MODE (Hex) | MODE ID | Frequency Step (s) | Spectra (s) | High Freq. Offset (s) | Low Freq. Offset (s) | Notes |

| 01 | CR-2 | 0.5 | 4.0 | 0.425 | 0.4325 | | | 02 | CR-3 | 1.2 | 9.6 | 1.125 | 1.1325 | | | 03 | CR-4 | 4.8 | 38.4 | 0.425 | 0.4325 | | | 04 | CR-5 | 9.6 | 76.8 | 0.425 | 0.4325 | | | 05 | CR-6 | 12.0 | 96.0 | 0.9275 | 0.935 | | | 06 | CR-7 | | | | | Not implemented | | 07 | CR-1 | 0.5 | 4.0 | 0.225 | 0.2325 | | | 08 | GS-10A | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 0A | GS-3 | 0.5 | 4.0 | 0.425 | 0.4325 | | | 0C | GS-7 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 0E | GS-6 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 16 | OC-2 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 17 | OC-1 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 18 | †CR-5A | 0.5 | 4.0 | 0.425 | 0.4325 | | | 19 | GS-10 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 1A | GS-8 | 0.5 | 4.0 | 0.425 | 0.4325 | Same as GS-3 | | 1D | †UV-5A | 0.5 | 4.0 | 0.425 | 0.4325 | Same as CR-5A | +--------------------------------------------------------------------------------------------------------------------------+

†In CR-5A and UV-5A, the PWS is cycled at its 0.5 s per Frequency Step or 4 s per Spectrum Rate, but four Measurements are summed onboard in 10-bit Accumulators and these 10-bit Sums are downlinked. On the Ground, the Sums are divided by four, hence providing, in a sense, 16 s Averages. One of every 12 Sets of Sums is dropped onboard in order to avoid LECP Stepper Motor Interference.

• Data Processing
• ===============

The Spectrum Analyzer Data are a continuous (where Data are available) low resolution Data Set which provides Wave Intensity as a Function of Frequency (sixteen log-spaced Channels) and Time (one Spectrum per Time Intervals ranging from 4 s to 96 s, depending on Telemetry Mode). The Data are typically plotted as Amplitude versus Time for one or more of the Channels in a Strip-Chart like Display, or can be displayed as a Frequency-Time Spectrogram using a Grayscale or Color Bar to indicate Amplitude. With only sixteen Channels, it is usually best to stretch the Frequency Axis by interpolating from one frequency Channel to the next either linearly or with a Spline Fit. One must be aware if the Frequency Axis is stretched that more Resolution may be implied than is really present. The Voyager PWS Calibration Table is given in an ASCII Text File named VG2PWSCL.TAB (for Voyager 2). This provides Information to convert the uncalibrated 'Data Number' Output of the PWS sixteen-channel Spectrum Analyzer to calibrated Antenna Voltages for each frequency Channel. Following is a brief Description of this File and a Tutorial in its Application.

Descriptive Headers have been removed from this File. The Columns included are IDN, ICHAN01, ICHAN02, ICHAN03, ICHAN04, ICHAN05, ICHAN06, ..., ICHAN16.

The first Column lists an uncalibrated Data Number followed by the corresponding Value in calibrated Volts for each of the sixteen Frequency Channels of the PWS Spectrum Analyzer. Each

The SMTC rates pavement using a scale developed by the New York State Department of Transportation. While traveling at posted speed limit, the rater will make an assessment from inside the vehicle of each pavement segment ranging from 1 (completely impassable) to 10 (new pavement) based on the frequency and severity of distresses appearing on the pavement surface.SMTC staff have attended several training sessions led by the NYSDOT on the usage of this scale. Additional information can be found in the SMTC's Bridge and Pavement Condition Management System report and the City of Syracuse Supplemental Pavement Rating documentation, which can be found on the SMTC's website.Disclaimer: The data is for planning purposes only, and the SMTC does not guarantee the accuracy or completeness of the information.Data Dictionary:BPID: Unique identifier to for this road segment, to keep it tied to our road network.Miles: Length of the road in Miles.Street Name: The name of the road. Only the Street name suffix (Ave, Rd, or St) and direction (N, S, E, or W).Segment Start: Cross street that this section of road is measuring from.Segment End: Cross street that this section of road is measuring to.Block: 100 Level Block number that this section of the Street Name is referencing.Ward: City of Syracuse Ward that this street segment is located in.Width Feet: Width of the road segment in feet.Rating2023: The rating that SMTC has assigned to this section of road. The SMTC rates pavement using a scale developed by the New York State Department of Transportation. While traveling at posted speed limit, the rater will make an assessment from inside the vehicle of each pavement segment ranging from 1 (completely impassable) to 10 (new pavement) based on the frequency and severity of distresses appearing on the pavement surface. SMTC staff have attended several training sessions led by the NYSDOT on the usage of this scale.Codeset Name: Roads with a score of 9 or 10 are considered ExcellentCodeset Name: Roads with a score of 7 or 8 are considered GoodCodeset Name: Roads with a score of 6 is considered Fair.Codeset Name: Roads with a score of 1-5 are considered Poor.Codeset Name: In addition to the 1-10 values, the SMTC applies a value of 0 or Unrated, to a very small percentage of roads. In most instances, Unrated roads are either under construction at the time of rating, or consist of materials not suited for pavement rating, such as brick or concrete bridge deck.Rating Category: Rating of the road based on the information above.Funtional Classification: Functional classification describes the importance of a particular road or network of roads to the overall system and, therefore, is critical in assigning priorities to projects and establishing the appropriate highway design standards to meet the needs of the traffic served. This is a system that divides roads into different categories based on how they are most used, as well as determining which projects can get federal funding. FC code is the number assigned to this.Date Collected: The date that the information that this rating was determined was collected. Time is entered in MM/DD/YYY, HH:MM Format.

Spectrogram plots in GIF format derived from Voyager 1 Planetary Radio Astronomy (PRA) Highband receiver daily files during Jupiter Encounter (1979-02-01 to 1979-04-13). These plots are available for both polarization channels and in both color and grayscale. The color scale of these plots represent the electric field power spectral density in units of millibels. Across the top of each spectrogram in the spacecraft and instrument name, the name of the binary data file that was used to create this plot, the polarization channel (Left or Right) and the date in the format YYMMDD. The data set provides 48 second resolution highband radio mean power data in units of millibels. The high-band receiver consisted of 128 channels of 200 kHz bandwidth each, with center frequencies spaced at 307.2 kHz intervals from 1.2 MHz to 40.4 MHz. The highband receiver was designed especially for the observation of Jovian decametric radio emissions. The PRA radiometer was usually operated routinely in the so-called POLLO sweeping mode, in which all 198 frequency channels of the high- and low-band receivers together were swept in 6 sec, dwelling at each channel for 25 msec. From one step to the next in the channel switching sequence, the antenna polarization sense was reversed, i.e., was changed from RH to LH or vice versa. Thus the time required for making a measurement of both the RH and LH intensity components at both senses of elliptical polarization at a given frequency was 12 sec. The data consists of successive averages of 4 pairs of RH and LH intensity measurements, each average spanning an interval of 48 sec. The data are calibrated and are given in units of 'millibels' which is 1000 times the log of the received power. Zero millbels corresponds to approximately 1.4 x 10^-21 W m^-2 Hz^-1, however, this value is never seen in practice. The minimum values detected, which includes receiver internal and spacecraft generated noise, are about 2300 to 2400 millibels, or about 3.5 x 10^-19 W m^-2 Hz^-1; even higher values are seen at the very lowest frequencies. Note: The polarization indicated is the received polarization, not necessarily the emitted polarization. Correct interpretation of the received polarization depends on the antenna plane orientation relative to the radio source. A good description of this concept can be found in Leblanc Y., Aubier M. G., Ortega-Molina A., Lecacheux A., 1987, J.Geophys. Res. 92, 15125 and in Wang, L. and Carr, T.D., Recalibration of the Voyager PRA antenna for polarization sense measurement, Astron. Astrophys., 281, 945-954, 1994. and references therein.

These experiments provide a detailed record of the oscillation of flexible rubber tubes as air flows through them. The tubes are mounted in an experimental rig illustrated in "Oscillation of Flexible Tubes - Onset and Subsequent Behaviour - The Effect of the Rig.txt".

The suction is controlled by a voltage between 0 and 10 volts, which tells the suction fan to operate between its minimum and maximum power. During an experiment this voltage is set by a computer to ramp up slowly to a maximum value, before ramping slowly down again. The ramping speed is chosen so that any behaviour seen has time to reach a quasi-steady state. Throughout this pressure recordings are made at the 4 indicated locations (pu,p1,p2,pd) as well as an acoustic pressure reading close to the flexible tube and a video recording of the rotameter that records the flowrate. Pressures are measured relative to the external atmospheric pressure.

The flexible tubes are held in place under a prescribed axial strain.

For each experimental run we have recorded the unstrained tube diameter (d), the unstrained tube length (l0), the unstrained tube wall thickness (h), the tube length in the experiment (l), the sampling rate for pressure readings (fs), the pressure in the upstream plenum (pu), just upstream of the tube (p1), just downstream of the tube (p2), and in the downstream plenum (pd), and the acoustic pressure (pa).

All pressures are measured relative to the environmental pressure, such that pressures below the environmental pressure are positive.

From the video of the rotameter we extract a flowrate for each frame of the video. We then use the acoustic pressure and the sound recording from the video to synchronise these flowrate readings with the pressure readings (pa,p1,p2,pu, and pd are all recorded simultaneously). Hence we obtain a set of flowrate values over time, labelled Q. These readings need to have their own separate time vector. Hence we obtain the following vectors of experimental data; the time vector for pressure readings (time), the time vector for flowrate (time_Q), and the flowrate (Q).

For each run we have traces of p1, p2, pu, pd, pa, and Q. These recordings generally reveal a complex set of self excited oscillations. One important metric that we explicitly extract is the onset frequency, denoted f. We define this as the first frequency that appears in p1, p2 and pa that is sustained for more than around 1 second. This is somewhat subjective, but we feel that it is worth having these figures recorded here. We also estimate the uncertainty in this frequency by looking at the variation in frequency seen at onset. Hence we again add the following data; frequency at onset (f), uncertainty in f (f_pm), flowrate at onset (Qo), uncertainty in Qo (Qo_pm), p1 at onset (p1o), uncertainty in p1o (p1o_pm), p2 at onset (p2o), uncertainty in p2o (p2o_pm).

In runs where self excited oscillations were not observed (f, f_pm, Qo, Qo_pm, p1o, p1o_pm, p2o, p2o_pm) all have the value "nan".

To investigate the effect of the upstream and downstream rigid tubes, experiments were completed where the same flexible tube was used but the rigid tubes up- and downstream of the flexible tube were changed. Therefore, we also store the upstream rigid tube length (lu) and the downstream rigid tube length (ld).

When investigating the effect of changing the rigid tube lengths, some runs were completed where the flow was kept deliberately low enough to prevent self excited oscillations in order to compare the broad band noise created in these cases with the oscillations. The runs in which self excited oscillations were produced and not produced are outlined in a table in "Oscillation of Flexible Tubes - Onset and Subsequent Behaviour - The Effect of the Rig.txt".

In the cases where self excited oscillations are not produced no flowrate data is recorded.

Numerical data is stored in a separate csv file for each run, called "runX.csv". These have been compressed, and so each file is stored as "runX.tar.bz2". The csv file is arranged in rows. The first column contains the symbol for the variable, and all remaining columns contain the data.

We also have a summary csv file called "run_summary.csv". This contains the 15 columns run_number, d, l0, h, l, f, f_pm, Qo, Qo_pm, p1o, p1o_pm, p2o, p2o_pm, lu, ld.

SPARC Intelligent Compaction Analyzer (ICA) DatasetThis dataset contains compaction-related data collected during the development and testing of the SPARC Intelligent Compaction Analyzer (ICA) kit. The data was gathered through various trials designed to:Fine-tune the electronics of the ICA device.Refine and validate data processing algorithms.Research and develop experimental and established Intelligent Compaction Measurement Values (ICMVs).Trial SequenceThe trial is conducted on a compaction area approximately 12 meters in length and 4 meters in width, with compaction performed on two lanes (A and B). The recorded data is geotagged using geohashes for spots named A0, A1, …, A12, B0, B1, …, B12. The data is then sorted to these points of interest for analysis.Initial Data Collection: ICMV and NDG (Nuclear Density Gauge) data is collected for the existing ground.Excavation: The area is excavated to a depth of 250 mm, and ICMV data is collected as a Premap for the excavated surface (UnderlineLayer).Layer 1 Compaction (Soil): Soil is placed as Layer 1, compacted up to 6 passes, and ICMV data is collected. At the end NDG data is collected.Layer 2 Compaction (UGM): UGM is placed as Layer 2 on top of the compacted soil layer, compacted up to 8 passes, and ICMV data is collected. At the end NDG data is collected.Final Data Collection: NDG data and Moisture data are collected at selected points.File Naming ConventionFiles in this dataset adhere to the following naming structure:[File Information Tags]-[Material Type]-[Layer Information]-[Timestamp].xlsxExample: Filtered_UGM_ExistingLayer_2024-05-23_16_01.xlsxFile Information Tags: Can include multiple tags describing the processing level or content of the file (e.g., "Raw," "Filtered," "Lanetagged").Material Type: The type of material being compacted (e.g., "UGM" for Unbound Granular Material).Layer Information: Details about the layer being compacted (e.g., "ExistingLayer," "UnderlineLayer," "Layer1").Timestamp: Date and time of data collection in the format YYYY-MM-DD_HH_MM.Data FilteringThe "Filtered" data files have undergone the following filtering steps:Speed Filter: Only data points with a speed greater than 0.65 km/h are included. This removes roller idling data based on the GNSS maximum HDOP value.Frequency Filter: Data points are selected where the fundamental frequency (acceleration_A1_f) falls within the range of 29 Hz to 31 Hz. This range corresponds to the roller's vibration frequency during the test and helps eliminate transient vibrations and non-vibration data.Dataset Structure[Trial_folder_date]├── [Code_for_plotting]├── [Plots]├── [ICMV_data]│ ├── Raw_[material_type] [layer][timestamp].xlsx│ ├── Filtered_[material_type] [layer][timestamp].xlsx│ ├── Lanetagged_Filtered_[material_type] [layer][timestamp].xlsx│ └── SortedAverage_Lanetagged_Filtered_[material_type] [layer][timestamp].xlsx└── [NDG_Moisture_data]├── Compaction Nuclear Field Report (AS 1289 5.8.1) [material and layer info].xlsx└── Moisture Content Report (AS 1289 2.1.1) [material and layer info].xlsxRaw_[material_type]_[layer]_[timestamp]: Contains raw data collected by the ICA kitFiltered_[material_type]_[layer]_[timestamp]: Contains speed and frequency filtered dataLanetagged_Filtered _[material_type]_[layer]_[timestamp]: Contains data with lane position tagSortedAverage_Lanetagged_Filtered_[material_type]_[layer]_[timestamp]: averaged data sorted by lane positionGeoTag_[material_type]_[layer]_[timestamp]: image showing the relative spatial boundaries of lane position tagsCompaction Nuclear Field Report (AS 1289 5.8.1) [material and layer info]: NDG dataMoisture Content Report (AS 1289 2.1.1) [material and layer info]: Moisture dataRoller SpecificationsType: Caterpillar CS44BDrum Mass: 3383 kgEccentric Moment Force: 3.32 kg·mDrum Length: 1.6764 mDrum Radius: 0.6223 mRoller Static Weight: 7209 kgFront Static Weight: 3518 kgRoller Vibration Frequency Range: 23.4-31.9 HzRoller Maximum Centrifugal Force: 13562 kgParameter List: Please refer to the ReadMe Document inside the trial folder.

Pollination is an important ecological process for plant reproduction. Understanding the differences in plant–pollinator interactions and pollinator importance across spatial scales is vital to determine the responses of these interactions to global changes. Continental and oceanic island systems provide us with an ideal model to examine the variation in plant–pollinator interactions. Here, we compared the differences in species composition, plant–pollinator network structure, and pollinator importance in communities between a continental island (Wanshan Island) and an oceanic island (Yongxing Island) in China. The results reveal highly dissimilar species composition between continental and oceanic islands that caused highly different plant–pollinator network structures. In particular, the oceanic island networks had higher network connectance, nestedness, and specialization than the continental island networks. For plants co-occurring on both islands, pollinator species richness and flower visitation rate were higher on the continental island than on the oceanic island. Plant niche overlap was higher on the oceanic island than on the continental island, while pollinator niche overlap was higher on the continental island than on the oceanic island in both entire network and co-occurring plant species subnetwork. Hymenoptera was the most important pollinator group in the continental island community, while Apidae was the most important in the oceanic island community. The results imply that oceanic island communities may be less vulnerable to disturbance, such as habitat alteration or destruction, than continental island communities, and provide implication insights into biodiversity conservation for pollinators on islands. Methods Study sites This study was conducted on Wanshan Island (continental island; 21°56′N, 113°44′E) and Yongxing Island (oceanic island; 16°49′N, 112°20′E). Wanshan Island belongs to the Wanshan archipelago (Zhuhai, Guangdong province, China) and was separated from the mainland due to the rising sea level during the Holocene (Wang, 2008). The distance between Wanshan Island and the city of Zhuhai is about 40 km. Yongxing Island belongs to the Xisha archipelago (Sansha, Hainan province, China), a group of coral islands (Paracel Islands) in the South China Sea that was formed in the Late Tertiary (Gong et al., 1996). The Yongxing Island is developed on coral reefs, and the distance between the archipelago and the southeast of Hainan Island of China is around 350 km. The areas of Wanshan and Yongxing islands are 8.2 km2 and 2.6 km2, respectively. Our field observations of plant–pollinator interactions were conducted from July 1 to August 30, 2018, on Yongxing Island and from July 1 to August 30, 2019, on Wanshan Island. Plant–pollinator interaction data collection To maximize the possibility of recording different pollinators of each plant species in the community, the focal plant approach was used to collect plant–pollinator interaction data on the two islands. We observed all plant species that were in full bloom in both island communities. For each plant species, pollinators were observed and recorded on sunny days without wind between 08:30 and 17:00 h. To minimize interference with pollinator behavior during observation, observers were located at a distance of about 1 m from the focal plant species. We recorded flower visits by pollinators during 15-min periods. For each focal plant species, we recorded the number of observed flowers or flower heads, all pollinator species that visited flowers, and the number of times flowers were visited by each pollinator species. We recorded those flower visitors whose bodies touched the reproductive organs for more than one second. These visits were defined to have occurred effectively, and these visitors were classified as potential pollinators (hereafter, named pollinators). For each plant species, the flower visitation rate was calculated as the number of visits by each pollinator species during a census, standardized by the number of flowers observed and the total number of censuses. Each focal plant species in the community was observed for two consecutive days. Field counts for morphologically identifiable pollinators (e.g., Apis cerana) were recorded based on prior experience. Pollinators that could not be morphologically identified to the species level in the field were first noted to the family level. These pollinators were caught by a sweep net and then transferred to centrifuge tubes containing ethanol for further identification by entomologists. All captured insect specimens were deposited at the South China Botanical Garden, Chinese Academy of Sciences. Composition of pollinator species between the two islands To investigate the differences in pollinator composition between Wanshan and Yongxing islands, we calculated and plotted the proportional abundance of pollination functional groups recorded across our sampling times. The pollinator functional group includes Apidae, Syrphidae, non-Apidae Hymenoptera, non-Syrphidae Diptera, Lepidoptera, Coleoptera, and Passeriformes (hereafter, Hymenoptera and Diptera represent all the Hymenoptera and Diptera, respectively). To investigate the variation in pollinator assemblages on the two islands, we first calculated and plotted the proportional abundance of pollinator groups of seven plant species that grow on both islands. To visualize the distances of pollinator assemblages visiting the same seven plant species on the two islands, we performed non-metric multidimensional scaling ordination using the vegan package in R v.4.1.3 (R Core Team, 2022). The Morisita–Horn index was chosen because it is not sensitive to species richness and sample size (Chao et al., 2006). To do so, an interaction matrix of the seven co-occurring plant species was constructed for each island, with rows representing the plant species, columns representing the pollinator species, and cell values indicating the interaction frequency between the plant and pollinator species. To statistically test if the pollinator assemblage of a co-occurring plant species was significantly different between the two islands, analysis of similarities (ANOSIM) was performed using the “anosim” function in the vegan package in R and corroborated by 9,999 permutations. The ANOSIM test statistic (global R) is a comparative measure of average ranking within and between a priori-defined groups. In this study, the dissimilarities of the pollinator assemblage of a co-occurring plant species were ranked, and then the mean ranked dissimilarities of the two islands were compared. Global R values vary from 0 to 1; values close to 1 indicate that replicates within a group are more similar to each other than to any replicates from different groups, and 0 implies no segregation into groups. The significance of the global R is determined by permuting the membership of objects in the groups. Plant–pollinator network structures We constructed two quantitative plant–pollinator networks from the interaction data, one for Wanshan and one for Yongxing island communities, using the frequency of visitation of the pollinator species to each plant species as a surrogate of interaction strength between plants and pollinators. Since the value of pollinator visitation frequency is decimal, not an integer, which is not suitable for the bootstrapped analysis, we standardized the flower visitation frequency by multiplying by 1000. We compared the values of several metrics of network structure between networks with and without multiplication by 1000, and these values are the same. We visualized the two plant–pollinator interaction networks in both island communities using the “plotweb” function in the R bipartite package. The plots included the weighted interaction frequency to show interactions proportional to the number of pollinator visits. Based on these two interaction matrices of the seven co-occurring plant species on the two islands, we visualized their plant–pollinator networks. Quantitative network metrics are more robust to sampling bias than qualitative networks, which are preferred for comparisons between communities. To examine the structure of the plant–pollinator networks, we calculated three network-level indices that are not sensitive to scale, with most of them being frequency-based and considering the frequency of interactions. Network connectance was calculated using the index of weighted connectance, which is the proportion of realized interactions in the network weighted by the interaction strength of each species. Network nestedness was calculated using the weighted nestedness metric based on overlap and decreasing fill (WNODF), which is a measure of the degree of nestedness for quantitative data, and it describes the extent to which species with fewer interactions are preferentially associated with a subset of species that interact with the most connected ones. Network specialization was calculated using the index H2′, which measures the level of interaction complementarity within the network. All network-level metrics were calculated using the “networklevel” function in the bipartite package. To ensure that sample size differences did not affect results, weighted connectance, WNODF, and H2' were bootstrapped 1,000 times using the bootstrapnet package. Overlapping niches are vital to maintain the complementarity in specialization of plant–pollinator interactions and provide pollinators with flexibility when foraging (Blüthgen & Klein, 2011). Given the importance of overlapping niches to plant–pollinator networks in redundancy of functional roles, we calculated the niche overlap of plants and pollinators that measures the extent to which plants share pollinators and vice versa using the “networklevel” function in the bipartite package (Dormann et al., 2021). Values of niche overlap vary

Based on over 5 million syncing email accounts, we can parse all transactional data in TikTok shop (and other e-commerce names) to see what individuals in each country are purchasing exactly on an SKU level. Average order value, discounts used, items bought, frequency of purchase, seller name, email ID, geolocation data all included.

The world's largest operators, financial institutions, consultancies and market research firms license our datasets for added granular insights. Contact michelle@measurable.ai to learn more or for some samples for backtesting.

Transport and communication are vital domains within the field of analytics, particularly in addressing safety and environmental concerns linked to the rapid growth of urban areas and increasing air traffic. Among the many risks aviation faces, bird strikes—collisions between aircraft and birds or other wildlife—pose a significant threat. These strikes can cause serious damage to aircraft, particularly jet engines, and have been responsible for some fatal accidents. Bird strikes are most likely to occur during critical flight phases such as take-off, climb, approach, and landing, when aircraft are at lower altitudes and bird activity is higher.

The dataset provided by the FAA, covering incidents from 2000 to 2011, offers a comprehensive overview of bird strikes in the U.S. It includes detailed visualizations and analyses across several key areas:

• Trends Over Time: Yearly distribution of bird strike incidents.
• Airline Impact: Analysis of the top 10 U.S. airlines affected by bird strikes.
• Airport Incidents: Identification of the 50 U.S. airports with the highest frequency of bird strike incidents.
• Economic Impact: Yearly costs incurred by airlines and the aviation industry due to bird strikes.
• Timing and Altitude: When and at what altitude most bird strikes occur.
• Flight Phase: The phase of flight during which strikes are most likely to happen.
• Impact Analysis: How bird strikes affect flight operations, including aircraft damage.
• Pilot Awareness: Correlation between pilot knowledge of potential bird strike risks and the severity of the incidents.

This dataset offers valuable insights into bird strike patterns, focusing on factors such as aircraft type, location, flight phase, and the specific species involved. By analyzing these variables, it helps identify risk factors and trends, supporting the development of strategies to reduce the frequency and impact of bird strikes, ultimately enhancing aviation safety and risk mitigation.

Features:

• AircraftType: The type of aircraft involved in the bird strike incident (e.g., "Airplane").
• AirportName: The name of the airport where the bird strike occurred (e.g., "LAGUARDIA NY", "DALLAS/FORT WORTH INTL ARPT").
• AltitudeBin: The altitude range (in feet) at which the bird strike occurred, divided into bins (e.g., "(1000, 2000]", "(30, 50]").
• MakeModel: The specific make and model of the aircraft involved (e.g., "B-737-400", "MD-80", "A-300").
• NumberStruck: The number of birds that were struck during the incident (e.g., "Over 100", "1", "26").
• NumberStruckActual: The actual number of birds that were struck during the incident (e.g., 859, 424, 261).
• Effect: The effect of the bird strike on the aircraft, indicating whether it caused any damage or not (e.g., "Engine Shut Down", "No damage", "Caused damage").
• FlightDate: The date of the bird strike incident (e.g., "11/23/00 0:00").
• Damage: A description of the damage caused by the bird strike (e.g., "Caused damage", "No damage").
• Engines: The number of engines on the aircraft involved in the bird strike (e.g., 2 engines).
• Operator: The airline or operator of the aircraft involved in the bird strike (e.g., "US AIRWAYS", "AMERICAN AIRLINES", "ALASKA AIRLINES").
• OriginState: The U.S. state where the aircraft originated (e.g., "New York", "Texas", "Washington").
• FlightPhase: The phase of flight during which the bird strike occurred (e.g., "Climb", "Landing Roll", "Approach", "Take-off run")
• ConditionsPrecipitation: The weather condition related to precipitation at the time of the bird strike (e.g., "None", "Some Cloud").
• RemainsCollected?: Indicates whether bird remains were collected after the strike (e.g., "True" or "False").
• RemainsSentToSmithsonian: Indicates whether the bird remains were sent to the Smithsonian Institution for study (e.g., "True" or "False").
• Remarks: Additional comments or notes related to the incident, including specific details like the number of birds involved, actions taken, or other observations (e.g., "FLYING UNDER A VERY LARGE FLOCK OF BIRDS", "BIRD REMAINS ON F/O WINDSCREEN").
• WildlifeSize: The size of the bird or wildlife involved in the strike (e.g., "Small", "Medium").
• ConditionsSky: The sky condition at the time of the bird strike (e.g., "No Cloud", "Some Cloud").
• WildlifeSpecies: The species of the bird or wildlife involved in the strike (e.g., "European starling", "Rock pigeon", "Unknown bird - medium").
• PilotWarned: Indicates whether the pilot was warned about the potential for a bird strike (e.g., "Y" for Yes, "N" for No).
• Cost: The cost incurred as a result of the bird strike (e.g., financial cost to repair damage or related expenses, usually in monetary value like 30,736).
• Altitude: The specific alt...

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Before the coronavirus (COVID-19) pandemic, 17 percent of U.S. employees worked from home 5 days or more per week, a share that increased to 44 percent during the pandemic. The outbreak of the COVID-19 pandemic accelerated the remote working trend, as quarantines and lockdowns made commuting and working in an office close to impossible for millions around the world. Remote work, also called telework or working from home (WFH), provided a solution, with employees performing their roles away from the office supported by specialized technology, eliminating the commute to an office to remain connected with colleagues and clients. What enables working from home?

To enable remote work, employees rely on a remote work arrangements that enable hybrid work and make it safe during the COVID-19 pandemic. Technology supporting remote work including laptops saw a surge in demand, video conferencing companies such as Zoom jumped in value, and employers had to consider new communication techniques and resources. Is remote work the future of work?

The response to COVID-19 has demonstrated that hybrid work models are not necessarily an impediment to productivity. For this reason, there is a general consensus that different remote work models will persist post-COVID-19. Many employers see benefits to flexible working arrangements, including positive results on employee wellness surveys, and potentially reducing office space. Many employees also plan on working from home more often, with 25 percent of respondents to a recent survey expecting remote work as a benefit of employment. As a result, it is of utmost importance to acknowledge any issues that may arise in this context to empower a hybrid workforce and ensure a smooth transition to more flexible work models.

Annotated base constructed to validate the methodological approach for detecting dustbathing events presented in e publication "Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis" (Fonseca et al., 2024. Ethology). For this experiment was performed where the behaviour of Japanese quail (Coturnix japonica), equipped with accelerometers, was captured through video recording within their home boxes. Experimental disign is presented in detail in Fonseca et al [1]. A total of 13 pairs of male/female quail were studied, named boxes 1-13, as stated in file names. The week before the experiment, a backpack was fitted onto the male, to promote habituation to it (Pellegriniet al., 2019). The backpack was 3D-printed in black plastic and had two elastic bands on their sides to be passed around the base of the animal's wings. On the morning of the experiment (between 9-11 am) the TechnoSmArt@ accelerometer-loggers Axyz was inserted into the backpack using a specially designed applicator to ensure synchronization between the acceleration time series and the two video recordings (top and side cameras). Tri-axial accelerometers were set to gather data with a sampling frequency of 25 Hz (i.e. 25 data points per second) based on previous studies that show the possibility of high-speed transitions between behaviours in this species [2]. A wire bar wall partition was inserted in the middle of the home box, dividing it into two separate, equal-sized, compartments. After the placement of accelerometers in the backpack, males were then positioned in one of these two home box compartments. Ten minutes later, the female companion bird was placed into the adjacent compartment. After a second 10-minute period, the wall partition was lifted, allowing the birds to interact. The respective video recordings began before the placement of the accelerometer and ended after the accelerometer was removed. Testing lasted 6h, thus given the 25Hz sampling frequency of the accelerometer, for each male at least 540,000 time points were obtained for the three axial compoenets (x, y and z) of the acceleration vector, corresponding to dataset columns ax,ay and az, respectively. For the second aim of this study, the last group (box 13) was recorded over a week-long period to demonstrate the methodology´s potential for studying dustbathing dynamics, such as daily rhythms, in long-term studies.Front and side video recordings were first scanned by an observer trained to detect dustbathing events. By strict definition, dustbathing can be characterized as a precise and orderly sequence of movements consisting of (a) pecking alternately from side to side with a closed beak; (b) scratching (one foot at a time) while sitting or squatting; (c) tossing the dust with the wings and undulating the body underneath the dust shower; and (d) occasionally rubbing head and/or body parts in the dust. Movements (b) and (c), and sometimes (a) and (d), are repeated a variable number of times. Since the initial pecking and scratching also appear in different behavioural contexts, the dust toss and body roll (undulation) were considered firm indicators of dustbathing. The time of beginning and end of each dustbathing event were determined from video recordings. Pauses longer than 5s, movement away from the site of dustbathing (usually preceded by standing and shaking), and/or the performance of another behaviour, marked the end of each dustbathing event. The annotated database was constructed by assigning the correspondence between accelerometer data and video recordings, with each time point (i.e.column labeled time, expresend in seconds) from the accelerometer annotated with a label indicating whether the bird had been observed dustbathing. Labels for the dustbathing column are indicated with a 0 if the behavior is not being performed and a 1 if behavior is being performed at a given time point.Maximum power of the y-axes component of the acceleration vector (ay) was estimated using the complex Morlet mother wavelet as descibed in [1]. Code provided is [3,4]. The squared absolute value of the complex wavelet coefficient is the power. When depicted in a magnitude scalogram the resulting plot is called a spectrogram (Flesia et al., 2022). The maximum value of power estimated within the range of scales 25 – 60 s is presented in the dataset as power spectrum coefficient (PSC) for the three axes, columns PSC_x, PSC_y and PSC_z.

This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.

The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.

Feature extraction pipeline

For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.

First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.

Next, we process both the refined and the raw time series to extract the features, as follows:

Statistical Features: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (avg), the standard deviation (std), and the coefficient of variation (cov). Features prefixed with avg, std, or cov, have been extracted as described here.

Spectral Analysis Features: We first apply the DFT to the raw time series. Then, we measure:

The frequency of highest energy (we prefix its name with top_frequency);

The periodicity of the series in the frequency domain;

The energy at the daily and weekly frequencies (daily_energy and weekly_energy);

The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (w_top_frequency, w_periodicity, w_daily_energy, w_weekly_energy);

The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (distance_from_sine).

The string b_day in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).

The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.

Source code

The Python source code developed to engineer and evaluate the embeddings is available here.