This is the supplemental data set for "Instantaneous habitable windows in the parameter space of Enceladus' ocean".nominal_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the nominal salt case, with [Cl] = 0.1m and [DIC] = 0.03m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K. high_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the high salt case, with [Cl] = 0.2m and [DIC] = 0.1m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K.low_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the low salt case, with [Cl] = 0.05m and [DIC] = 0.01m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K.CO2_activity_uncertainty.xlsx collects the activity of CO2 from the three files above into a single sheet. This is plotted in supplemental figure S2.independent_samples.zip contains a further 20 figures which show the variance caused by solely each of [CH4], [H2], n_ATP and k at a fixed temperature or pH as indicated by the file name. These show the deviation from the nominal log10(Power supply) e.g. Figure 3 in the main text if the named parameter were allowed to vary within its uncertainty defined in Table 1 in the main text.

Brazil Average Nominal Wages: Actual Earnings: Amazonas: Private Sector: Unregistered data was reported at 1,031.000 BRL in Mar 2019. This records an increase from the previous number of 898.000 BRL for Dec 2018. Brazil Average Nominal Wages: Actual Earnings: Amazonas: Private Sector: Unregistered data is updated quarterly, averaging 965.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,343.000 BRL in Mar 2016 and a record low of 835.000 BRL in Sep 2018. Brazil Average Nominal Wages: Actual Earnings: Amazonas: Private Sector: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Major differences from previous work: For level 2 catch: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines with identical strata but different effort units are duplicates reporting the same fishing activity with different measurement units. It is indeed not possible to infer strict equivalence between units, as some contain information about others (e.g., Hours.FAD and Hours.FSC may inform Hours.STD). in the case of WCPFC data, effort records were also kept in all originally reported units. Here, duplicates do not necessarily share the same “fishing_mode”, as SETS for purse seiners are reported with an explicit association to fishing_mode, while DAYS are not. This distinction allows SETS records to be separated by fishing mode, whereas DAYS records remain aggregated. Some limited harmonization—particularly between units such as NET-days and Nets—has not been implemented in the current version of the dataset, but may be considered in future releases if a consistent relationship can be established.

Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Unregistered data was reported at 1,603.000 BRL in Mar 2019. This records an increase from the previous number of 1,316.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Unregistered data is updated quarterly, averaging 1,147.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,635.000 BRL in Sep 2018 and a record low of 812.000 BRL in Jun 2012. Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Sample data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Second International Diagnostic Competition project page for more information.

Major differences from previous work: For level 2: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets.

Average Nominal Wages: Actual Earnings: Acre: Private Sector: Registered data was reported at 1,831.000 BRL in Mar 2019. This records an increase from the previous number of 1,607.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Acre: Private Sector: Registered data is updated quarterly, averaging 1,305.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,831.000 BRL in Mar 2019 and a record low of 977.000 BRL in Jun 2012. Average Nominal Wages: Actual Earnings: Acre: Private Sector: Registered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

MC simulation QCD jet nominal samples from the ATLAS experiment

The EFW Burst Modes provide targeted Measurements over Brief Time Intervals of 3-D Electric Fields, 3-D Wave Magnetic Fields, and Spacecraft Potential. There are two EFW Burst Modes: BURST1 (B1), Medium-Rate 512 samples/s nominal and BURST2 (B2), Higher-Rate, 16384 samples/s nominal. The Burst 1 Mode Data includes three components of the Electric Field (E12_B1, E34_B1, E56_B1), six Components of the Spacecraft-Sensor Potential (V1_B1 through V6_B1), and three Components of the AC Magnetic Field (SCM_U_B1, SCM_V_B1, SCM_W_B1 from the EMFISIS Search Coil Magnetometer). The Burst 2 Mode Data returns a similar Complement of Electric Field (E_12ac_B2, E34ac_B2, E56ac_B2), Search Coil (SCM_B2, SCM_2B2, and SCM_B2 again from the EMFISIS Search Coil Magnetometer) and Single-ended Potential Measurements (V1ac_B1 through V6ac_B2) with the exception that in the Default Mode the Single-ended Potential and Electric Field Signals are AC coupled with a higher Gain. All Quantities are in "uvw" Coordinates where "u" and "v" are the Sensor Coordinates rotating with the Spacecraft and "w" points along the Spacecraft Spin Axis. Burst Waveform CDF Files are available. The three Data Types available are the Electric Field "E" the Searchcoil Magnetic Field "MSC" and Antenna Potential "V". The Suffix on each of these is either "B1" or "B2". B1 or Burst 1 is the Human-in-the-Loop Burst Type, meaning that both Collection and Playback (for arbitrary Lengths of Time) are requested on the Ground. B2 or Burst 2 is automatically telemetered as short Bursts based on an onboard Triggering Algorithm, typically set to trigger on large Amplitude Signals near 1 kHz. Sample Rates for B1 and B2 can and are changed depending on varying Science Goals.

Major differences from v1: For level 2 catch: Catches and number raised to nominal are only raised to exactly matching stratas or if not existing, to a strata corresponding with UNK/NEI or 99.9. (new feature in v4) When nominal strata lack specific dimensions (e.g., fishing_mode always UNK) but georeferenced strata include them, the nominal data are “upgraded” to match—preventing loss of detail. Currently this adjustment aligns nominal values to georeferenced totals; future versions may apply proportional scaling. This does not create a direct raising but rather allows more precise reallocation. (new feature in v4) IATTC Purse seine catch-and-effort are available in 3 separate files according to the group of species: tuna, billfishes, sharks. This is due to the fact that PS data is collected from 2 sources: observer and fishing vessel logbooks. Observer records are used when available, and for unobserved trips logbooks are used. Both sources collect tuna data but only observers collect shark and billfish data. As an example, a strata may have observer effort and the number of sets from the observed trips would be counted for tuna and shark and billfish. But there may have also been logbook data for unobserved sets in the same strata so the tuna catch and number of sets for a cell would be added. This would make a higher total number of sets for tuna catch than shark or billfish. Efforts in the billfish and shark datasets might hence represent only a proportion of the total effort allocated in some strata since it is the observed effort, i.e. for which there was an observer onboard. As a result, catch in the billfish and shark datasets might represent only a proportion of the total catch allocated in some strata. Hence, shark and billfish catch were raised to the fishing effort reported in the tuna dataset. (new feature in v4, was done in Firms Level 0 before) Data with resolution of 10degx10deg is removed, it is considered to disaggregate it in next versions. Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. (as v3) Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. (as v3) Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. (as v3) Strata for which catches in tons are raised to match nominal data have had their numbers removed. (as v3) Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. (as v3) Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. (as v3) The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") (as v3) This results in a raising of the data compared to v3 for IOTC, ICCAT, IATTC and WCPFC. However as the raising is more specific for CCSBT, the raising is of 22% less than in the previous version. Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines wi

Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Unregistered data was reported at 1,005.000 BRL in Mar 2019. This records a decrease from the previous number of 1,026.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Unregistered data is updated quarterly, averaging 956.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,149.000 BRL in Jun 2016 and a record low of 667.000 BRL in Dec 2012. Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Average Nominal Wages: Actual Earnings: Ceará: Domestic Worker: Unregistered data was reported at 504.000 BRL in Mar 2019. This records an increase from the previous number of 472.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Ceará: Domestic Worker: Unregistered data is updated quarterly, averaging 447.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 504.000 BRL in Mar 2019 and a record low of 313.000 BRL in Jun 2012. Average Nominal Wages: Actual Earnings: Ceará: Domestic Worker: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Brazil Average Nominal Wages: Usual Earnings: Amazonas: Government Sector: Unregistered data was reported at 1,654.000 BRL in Mar 2019. This records a decrease from the previous number of 1,714.000 BRL for Dec 2018. Brazil Average Nominal Wages: Usual Earnings: Amazonas: Government Sector: Unregistered data is updated quarterly, averaging 1,315.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,928.000 BRL in Jun 2016 and a record low of 1,032.000 BRL in Jun 2012. Brazil Average Nominal Wages: Usual Earnings: Amazonas: Government Sector: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD001: Continuous National Household Sample Survey: Average Nominal Wages: Usual Earnings.

Average Nominal Wages: Actual Earnings: Amazonas: Domestic Worker: Unregistered data was reported at 630.000 BRL in Mar 2019. This records an increase from the previous number of 609.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Amazonas: Domestic Worker: Unregistered data is updated quarterly, averaging 570.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 662.000 BRL in Mar 2016 and a record low of 438.000 BRL in Mar 2012. Average Nominal Wages: Actual Earnings: Amazonas: Domestic Worker: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Registered data was reported at 3,333.000 BRL in Mar 2019. This records a decrease from the previous number of 3,936.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Registered data is updated quarterly, averaging 1,764.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 3,936.000 BRL in Dec 2018 and a record low of 992.000 BRL in Sep 2012. Average Nominal Wages: Actual Earnings: Alagoas: Government Sector: Registered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Brazil Average Nominal Wages: Actual Earnings: Acre: Government Sector: Registered data was reported at 2,779.000 BRL in Mar 2019. This records an increase from the previous number of 2,434.000 BRL for Dec 2018. Brazil Average Nominal Wages: Actual Earnings: Acre: Government Sector: Registered data is updated quarterly, averaging 2,074.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 2,914.000 BRL in Mar 2018 and a record low of 1,214.000 BRL in Jun 2012. Brazil Average Nominal Wages: Actual Earnings: Acre: Government Sector: Registered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Average Nominal Wages: Actual Earnings: Acre: Domestic Worker: Unregistered data was reported at 573.000 BRL in Mar 2019. This records an increase from the previous number of 513.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Acre: Domestic Worker: Unregistered data is updated quarterly, averaging 482.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 573.000 BRL in Mar 2019 and a record low of 386.000 BRL in Sep 2013. Average Nominal Wages: Actual Earnings: Acre: Domestic Worker: Unregistered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Registered data was reported at 1,641.000 BRL in Mar 2019. This records an increase from the previous number of 1,568.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Registered data is updated quarterly, averaging 1,373.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,757.000 BRL in Mar 2016 and a record low of 956.000 BRL in Jun 2012. Average Nominal Wages: Actual Earnings: Amapá: Private Sector: Registered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

Average Nominal Wages: Actual Earnings: Amapá: Government Sector: Registered data was reported at 1,364.000 BRL in Mar 2019. This records an increase from the previous number of 1,292.000 BRL for Dec 2018. Average Nominal Wages: Actual Earnings: Amapá: Government Sector: Registered data is updated quarterly, averaging 1,811.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 6,916.000 BRL in Dec 2016 and a record low of 1,051.000 BRL in Dec 2015. Average Nominal Wages: Actual Earnings: Amapá: Government Sector: Registered data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.