Overview

Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m²s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.

Info

ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3

barplot_R.R -> code to generate bar plot in R statistic 3.3.3

boxplotv2.R -> code to generate boxplot in R statistic 3.3.3

pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.

who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.

who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.

Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content

ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.

Consider citing our work.

Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Abstract

The Occupational Wages Survey (OWS) generates statistics for wage and salary administration and for wage determination in collective bargaining negotiations. This nationwide biennial survey covers establishments employing at least 20 workers.

The OWS is one of the designated statistical activities in E.O. 352 (s.1996) that designates those critical for decision making by the government and the private sector. Moreover, the data category average monthly occupational wage rates in selected occupation is among those listed by the Philippine government under the Special Data Dissemination Standard (SDDS) of the International Monetary Fund. The SDDS serves as reference to member countries in the dissemination of economic and financial data to the public.

Geographic coverage

National coverage, 17 administrative regions

Analysis unit

Establishment

Universe

The survey covers agricultural and non-agricultural establishments employing 20 or more workers except central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, activities of households as employers of domestic personnel, undifferentiated goods-and-services-producing activities of households for own use and activities of extra-territorial organizations and bodies.

Pre-determined industries for wage monitoring now total to 50 due to the inclusion of agriculture, forestry and fishery; and the splitting and merging of original domains with the adoption of the 2009 PSIC.

Inclusion of new domains: - Crop and Animal Production, Hunting and Related Service Activities; Forestry and Logging (A01/A02) - Fishing and Aquaculture (A03) - Manufacture of Basic Pharmaceutical Products and Pharmaceutical Preparation (C21)

Splitting of original domains: - Publishing and Printing (D221/D222/D223 of 1994 PSIC as amended) into Printing and Reproduction of Recorded Media (C18); and Publishing Activities (J58) - Supporting and Auxiliary Transport Activities; Activities of Travel Agencies (I63 of 1994 PSIC as amended) into Warehousing and Support Activities for Transportation (H52); and Travel Agency, Tour Operator, Reservation Service and Related Activities (N79)

Merging of original domains: - Banking Institutions except Central Banking (J65 excl. J6510 of 1994 PSIC as amended) and Non-Bank Financial Intermediation (J66 of 1994 PSIC as amended) into Financial Service Activities except Insurance, Pension Funding and Central Banking (K64 excl. K6411)

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2014 BLES Survey Sampling Frame (2014 SSF) is an integrated list of establishments culled from the updated 2012 BLES Survey Sampling Frame based on the status of establishments reported in the 2011/2012 BLES Integrated Survey (BITS) and 2012 Occupational Wages Survey (OWS). Other sources were Lists of Establishments from the National Statistics Office (2012), DOLE Regional Office IV-B,and the BLES Job Displacement Monitoring System (JDMS).

Sampling design: The OWS is a sample survey of agricultural and non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for detailed industry groupings.

Sample size: For 2014 OWS, the number of establishments covered was 8,399, of which, 6,595 were eligible units.

Mode of data collection

Other [oth]

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by PSA and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2014 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2014 OWS; results of the 2012 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color.

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the PSA Central Office and PSA Field Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in ensuring that the correct occupational sheet has been furnished to the respondent.

Selected Statistics from 2012 OWS (Page 12) The results of the 2012 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The LSRSD personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Response rate

The response rate in terms of eligible units was 87.2%.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

BackgroundClinical trial results registries may contain relevant unpublished information. Our main aim was to investigate the potential impact of the inclusion of reports from industry results registries on systematic reviews (SRs).MethodsWe identified a sample of 150 eligible SRs in PubMed via backward selection. Eligible SRs investigated randomized controlled trials of drugs and included at least 2 bibliographic databases (original search date: 11/2009). We checked whether results registries of manufacturers and/or industry associations had also been searched. If not, we searched these registries for additional trials not considered in the SRs, as well as for additional data on trials already considered. We reanalysed the primary outcome and harm outcomes reported in the SRs and determined whether results had changed. A “change” was defined as either a new relevant result or a change in the statistical significance of an existing result. We performed a search update in 8/2013 and identified a sample of 20 eligible SRs to determine whether mandatory results registration from 9/2008 onwards in the public trial and results registry ClinicalTrials.gov had led to its inclusion as a standard information source in SRs, and whether the inclusion rate of industry results registries had changed.Results133 of the 150 SRs (89%) in the original analysis did not search industry results registries. For 23 (17%) of these SRs we found 25 additional trials and additional data on 31 trials already included in the SRs. This additional information was found for more than twice as many SRs of drugs approved from 2000 as approved beforehand. The inclusion of the additional trials and data yielded changes in existing results or the addition of new results for 6 of the 23 SRs. Of the 20 SRs retrieved in the search update, 8 considered ClinicalTrials.gov or a meta-registry linking to ClinicalTrials.gov, and 1 considered an industry results registry.ConclusionThe inclusion of industry and public results registries as an information source in SRs is still insufficient and may result in publication and outcome reporting bias. In addition to an essential search in ClinicalTrials.gov, authors of SRs should consider searching industry results registries.

Abstract

The National Institute of Statistics of Rwanda (NISR) introduced the Labour Force Survey (LFS) program to avail statistics on employment and labour market in Rwanda on a continuous basis, providing bi-annual estimates of the main labour force aggregates. The main objective of the survey is to collect data on the size and characteristics of the labour force, employment, unemployment and other labour market characteristics of the population. The survey was also designed to measure different forms of work, in particular, own-use production work and other components of labour underutilization including time-related underemployment and potential labour force in line with the new international standards, adopted by the 19th International Conference of Labour Statisticians (ICLS) in 2013.

Geographic coverage

Labour force survey data are at the National level coverage but Employment and Labour force participation rate are represented at the District level as well as by residential area.

Analysis unit

Household and individual

Universe

The target population eligible for Labor force survey is 16 years old and above resident of selected households. However, the survey also collected data on certain particular labour-market related issues such as income from employment, migrant workers and workers with disabilities. The survey consider all persons living in private households. It excludes the institutional population permanently residing in houses such as hostels; health resorts; correctional establishments etc., as well as persons living in seasonal dwellings not covered in the survey. It also excludes workers living at their work-sites.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size determination in most household-based surveys with multi-stage stratified design is based on the principle of first calculating the required sample size for a single «domain» assuming a simple random sample design and no non-response. A domain is a well-defined population group for which estimates with pre-determined accuracy are sought. The results are then extended to allow for non-response and deviation from simple random sampling.

The sample design of the LFS is a two-stage stratified design according to which at the first stage of sampling, a stratified sample of enumeration areas from the latest population census is drawn with probabilities proportional to size measured in terms of the census number of households or census number of household members, and at the second stage of sampling, a fixed number of sample of households is selected with equal probability within each sample enumeration areas. Finally, all household members in the sample households are selected for survey interviewing.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire of the Rwanda Labour Force Survey 2018 in its present form contains a total of 149 questions organized into 9 sections and a cover page, dealing with following topics: A. Household roster (All Household member) B. Education (Person with 14 years and above) C. Identification of employed, time-related underemployed, unemployed and potential labour force (Person with 14 years and above) D. Characteristics of main job/activity (Person with 14 years and above) E. Characteristics of secondary job/activity (Person with 14 years and above) F. Past employment (Person with 14 years and above) G. Own-use production of goods and services (Person with 14 years and above) H. Subsistence foodstuff production (Person with 14 years and above & Household) I. Housing and household assets (Household)

Not all questions are addressed to every household member. For children below 14 years of age, a minimum number of questions are asked. For older youngsters and adults 14 years of age and above, the number of questions depends on the situation and activities of the person during the reference period. The basic reference period is the last 7 days prior to the date of the interview. For certain questions, however, other reference periods are used. In each case, the relevant reference period is indicated in the text of the question.

Cleaning operations

Since August 2017 an electronic data collection system has replaced paper based questionnaire and data were collected using computerized assisted interview (CAPI). Data was uploaded to NISR severs from the field via wireless network channel by synchronizing every day with the NISR server. It was carried every day to have a daily back up of data. All the activity of codification were also done to the field by interviewers who were trained. Several questions with textual responses were pre-coded in tabled in cascaded way. These concerned education (major field of study in highest qualification attained, and subject of training), occupation and branch of economic activity (at main and secondary job and past employment experience). They were coded into the corresponding national standard classifications using on-screen coding with corresponding dictionaries in Kinyarwanda. Coding of geographic areas and addresses was incorporated in the data entry program as look-up. Following coding, responses of each questionnaire were edited for blanks, missing values, duplicates, out-of-range values, and inconsistencies such as no head of household or age of child greater than age of head of household using developed batches of controlling inconsistence in CsPro and Stata. Edit rules were developed for consistency checks on questions related to the measurement of the main labour force variables, including employment, unemployment, multiple jobholding, total hours usually worked at all jobs, total hours actually worked at all jobs, status in employment at main job, etc. Corrections were made mostly with reference to the original physical questionnaire

Response rate

The response rate for labor force survey 2019 is 98.6%

= mean; SD = standard deviation; BDI-score = Beck Depression Inventar score; VAS relative score = relative score in Visual Analogue Scale; RCFT IR = Immediate Recall trial in the Rey Complex Figure Test; SDMT = Symbol Digit Modalities Test; VLMT total = total number of correctly recalled items in trials 1 to 5 of the Verbaler Lern- und Merkfähigkeitstest; VLMT 5–7 = “trial 7”–“trial 5” difference in the VLMT; PASAT = Paced Auditory Serial Addition Test; TMT = Trail Making Test; RWT p/s = phonemic/semantic subtests of the Regensburger Wortflüssigkeits-Test; 9-HPT = 9-hole peg test; WST-z-score = Wortschatztest z-score.