Students use U.S. Geological Survey (USGS) real-time, real-world seismic data from around the planet to identify where earthquakes occur and look for trends in earthquake activity. They explore where and why earthquakes occur, learning about faults and how they influence earthquakes. Looking at the interactive maps and the data, students use Microsoft Excel to conduct detailed analysis of the most-recent 25 earthquakes; they calculate mean, median, mode of the data set, as well as identify the minimum and maximum magnitudes. Students compare their predictions with the physical data, and look for trends to and patterns in the data. A worksheet serves as a student guide for the activity.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

Below are frequency comparisons of different models with experiment Note Modeshapes aren't very descriptive for higher modes. There is coupling between them so this is just an approximate naming scheme. See modeshape plots for more details. PDF files are provided with figures of the modeshapes for selected FEM TET10 model (Nov 2011) (CASE 10) Hex8 Modeshapes (CASE 4) TET10 no modelcart (CASE 5) HIRENASD TET model with modelcart - new OML HIRENASD HEX 8 Wing only model Mode 1 Mode 1 Mode 2 Mode 2 Mode 3 Mode 3 Mode 4 Mode 4 Mode 5 Mode 5 Mode 6 Mode 6 Mode 7 Mode 7 Mode 8 Mode 8 Mode 9 Mode 9 Mode 10 Mode 10 Mode 11 Mode 12

Here you can find symbols and pictograms for all transport modes to use in your apps, products and other projects. Symbols and icons are available in various formats, while all can be found as vector files that can be opened directly in software such as Adobe Illustrator.

This repository contains all the raw data and raw images used in the paper titled 'Highly multi-mode hollow core fibres'. It is grouped into two folders of raw data and raw images. In the raw data there are a number of .dat files which contain alternating columns of wavelength and signal for the different measurements of transmission, cutback and bend loss for the different fibres. In the raw images, simple .tif files of the different fibres are given and different near field and far field images used in Figure 2.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

This dataset supports measure M.A.1 of SD 2023. The source of the data is the American Community Survey. Each row is the five year estimate for Means of Transportation to Work for Austin. This dataset can be used to gain insight into the estimated mode split for the commute to work in Austin. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/hm3r-8jfy

Context

Identify user’s transportation modes through observations of the user, or observation of the environment, is a growing topic of research, with many applications in the field of Internet of Things (IoT). Transportation mode detection can provide context information useful to offer appropriate services based on user’s needs and possibilities of interaction.

Content

Initial data pre-processing phase: data cleaning operations are performed, such as delete measure from the sensors to exclude, make the values of the sound and speed sensors positive etc...

Furthermore some sensors, like ambiental (sound, light and pressure) and proximity, returns a single data value as the result of sense, this can be directly used in dataset. Instead, all the other return more than one values that are related to the coordinate system used, so their values are strongly related to orientation. For almost all we can use an orientation-independent metric, magnitude.

Acknowledgements

A sensor measures different physical quantities and provides corresponding raw sensor readings which are a source of information about the user and their environment. Due to advances in sensor technology, sensors are getting more powerful, cheaper and smaller in size. Almost all mobile phones currently include sensors that allow the capture of important context information. For this reason, one of the key sensors employed by context-aware applications is the mobile phone, that has become a central part of users lives.

Inspiration

User transportation mode recognition can be considered as a HAR task (Human Activity Recognition). Its goal is to identify which kind of transportation - walking, driving etc..- a person is using. Transportation mode recognition can provide context information to enhance applications and provide a better user experience, it can be crucial for many different applications, such as device profiling, monitoring road and traffic condition, Healthcare, Traveling support etc..

Original dataset from: Carpineti C., Lomonaco V., Bedogni L., Di Felice M., Bononi L., "Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity", in Proceedings of the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 2018 [Pre-print available]

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

----> Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Provides agency-wide totals for track and roadway components. Data is from the National Transit Database in the 2022 and 2023 report years. These data include the types of track/roadway elements employed in transit operation, as well as the length and/or count of certain elements. This view is based off of the "2022 - 2023 NTD Annual Data - Track & Roadway (by Mode)" dataset, which displays the same data at a lower level of aggregation. This view displays the data at a higher level (by agency).

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. The dataset that this view references is based on the 2022 and 2023 Transit Way Mileage database files.

In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

In versions of the data tables from before 2015, you can find corresponding data in the file called "Transit Way Mileage - Rail Modes" and "Transit Way Mileage - Non-Rail Modes."

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

An open preclinical PET dataset. This dataset has been measured with the preclinical Siemens Inveon PET machine. The measured target is a (naive) rat with an injected dose of 21.4 MBq of FDG. The injection was done intravenously (IV) to the tail vein. No specific organ was investigated, but rather the glucose metabolism as a whole. The examination is a 60 minute dynamic acquisition. The measurement was conducted according to the ethical standards set by the University of Eastern Finland.

The dataset contains the original list-mode data, the (dynamic) sinogram created by the Siemens Inveon Acquisition Workplace (IAW) software (28 frames), the (dynamic) scatter sinogram created by the IAW software (28 frames), the attenuation sinogram created by the IAW software and the normalization coefficients created by the IAW software. Header files are included for all the different data files.

For documentation on reading the list-mode binary data, please ask Siemens.

This dataset can be used in the OMEGA software, including the list-mode data, to import the data to MATLAB/Octave, create sinograms from the list-mode data and reconstruct the imported data. For help on using the dataset with OMEGA, see the wiki.

This dataset (in .csv format), accompanying codebook and replication code serve as supplement to a study titled: “Does the mode of administration impact on quality of data? Comparing a traditional survey versus an online survey via a Voting Advice Application” submitted for publication to the journal: “Survey Research Methods”). The study involved comparisons of responses to two near-identical questionnaires administered via a traditional survey and through a Voting Advice Application (VAA) both designed for and administered during the pre-electoral period of the Cypriot Presidential Elections of 2013. The offline dataset consisted of questionnaires collected from 818 individuals whose participation was elicited through door-to-door stratified random sampling with replacement of individuals who could not be contacted. The strata were designed to take into account the regional population density, gender, age and whether the area was urban or rural. Offline participants completed a pen-and-paper questionnaire version of the VAA in a self-completing capacity, although the person administering the questionnaire remained present throughout. The online dataset involved responses from 10,241 VAA users who completed the Choose4Cyprus VAA. Voting Advice Applications are online platforms that provide voting recommendations to users based on their closeness to political parties after they declare their agreement or disagreement on a number of policy statements. VAA users freely visited the VAA website and completed the relevant questionnaire in a self-completing capacity. The two modes of administration (online and offline) involved respondents completing a series of supplementary questions (demographics, ideological affinity & political orientation [e.g. vote in the previous election]) prior to the main questionnaire consisting of 35 and 30 policy-related Likert-type items for the offline and online mode respectively. The dataset includes all 30 policy items that were common between the two modes, although only the first 19 (q1:q19) appeared in the same order and in the same position in the two questionnaires; as such, all analyses reported in the article were conducted using these 19 items only. The phrasing of the questions was identical for the two modes and is described per variable in the attached codebook.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

This data set gives the best available values for ion densities, temperatures, and velocities near Neptune derived from data obtained by the Voyager 2 plasma experiment. All parameters are obtained by fitting the observed spectra (current as a function of energy) with Maxwellian plasma distributions, using a non-linear least squares fitting routine to find the plasma parameters which, when coupled with the full instrument response, best simulate the data.

{"references": ["M. Businger et al., "Remote distribution of non-classical correlations over 1250 modes between telecom photons and 978 nm photons stored in 171Yb3+:Y2SiO5 crystal",\u00a0arXiv:2205.01481"]} Processed datasets corresponding to the Figures published in the article.

This dataset contains a consolidated view of Official Utilisation figures across all transport modes (train, metro, bus, ferry and light rail). Opal daily tap-on/tap-off data is aggregated to a total monthly figure representing the estimated number of trips across all transport modes. Starting July 1, 2024, the methodology for calculating trip numbers for individual lines and operators will change to more accurately reflect the services our passengers use within the transport network. This new approach will apply to trains, metros, light rail, and ferries, and will soon be extended to buses. Aggregations between line, agency, and mode levels will no longer be valid, as a passenger may use multiple lines on a single trip. Trip numbers at the line, operator, or mode level should be used as reported, without further combinations. The dataset includes reports based on both the new and old methodologies, with a transition to the new method taking place over the coming months. As a result of this change, caution should be exercised when analysing longer trends that utilise both datasets. More information on NRT ROAM can be accessed here

This dataset details data on hours worked by public transportation employees and the head counts of employees for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years at the mode and type of service level.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Agency Employees database files.

In years 2015-2021, you can find this data in the "Employees" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

1H-pyrrolo[3,2-h]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.

An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.

For the initial OPT and ANFREQ, the following files are provided: PQ_OPT_AFQ.engrad -- Gradient data PQ_OPT_AFQ.gbw -- Wavefunction PQ_OPT_AFQ.hess -- Hessian data PQ_OPT_AFQ.out -- Computation output PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ) PQ_OPT_AFQ.txt -- ORCA input file PQ_OPT_AFQ.xyz -- Optimized geometry

For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs: PQ_NFQ_0.####.hess -- Hessian data PQ_NFQ_0.####.out -- Computation output PQ_NFQ_0.####.txt -- ORCA input file

Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)

The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.

For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.

A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.