The Foreign Service Act of 1980 mandated a comprehensive revision to the operation of the Department of State and the personnel assigned to the US Foreign Service. As the statutory authority, the Foreign Affairs Manual (FAM), details the Department of Sta

Verze klasifikace CZ-CISS platná od 1. 5. 2014, zavedená sdělením ČSÚ č. 67/2014 Sb. Označována také: Klasifikace institucionálních sektorů a subsektorů (CZ-CISS) dle ESA 2010 Klasifikace CZ-CISS se skládá z 5 úrovní (sektor I, sektor II, sektor III, sektor IV, sektor V), které jsou reprezentovány samostatnými číselníky.

Dataset for Classification of Sections of Academic Papers

A dataset mapping sections of academic papers to one of the following section types: 0: Introduction 1: Background 2: Methodology 3: Experiments and Results 4: Conclusion The dataset was collected by taking the GROBID parses of academic papers in the ACL-OCL dataset and matching the section headings to one of the synonyms of each section type. Sections that did not have a match were disregarded. The following synonyms are… See the full description on the dataset page: https://huggingface.co/datasets/nhop/academic-section-classification.

Dataset population: Persons aged under 65 in households

NS-SeC of HRP

The National Statistics Socio-economic Classification (NS-SeC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification.

To assign a person aged 16 to 74 to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. Information about the classification is available here: NS-SEC rebased on SOC2010.

For 'Long-term unemployed', the year last worked is 2009 or earlier. In 2011 Census results, because the census did not ask a question about the number of employees at a person's workplace, the reduced method of deriving NS-SeC (which does not require this information) is used.

The concept of a Household Reference Person (HRP) was introduced in the 2001 Census (in common with other government surveys in 2001/2) to replace the traditional concept of the 'head of the household'. HRPs provide an individual person within a household to act as a reference point for producing further derived statistics and for characterising a whole household according to characteristics of the chosen reference person.

This dataset is about news. It has 15 rows and is filtered where the keywords includes Farms-Poland-History and the section is business. It features 3 columns: polarity sentiment score, and classification.

This dataset is about news. It has 1 row and is filtered where the entities includes companies, the keywords includes BlackRock and the section is culture. It features 3 columns: polarity sentiment score, and classification.

This dataset is about news. It has 19 rows and is filtered where the entities includes cities, the keywords includes El Paso and the section is politics. It features 3 columns: polarity sentiment score, and classification.

Dataset population: Persons aged 16 to 74

Highest level of qualification

The highest level of qualification is derived from the question asking people to indicate all types of qualifications held. People were also asked if they held foreign qualifications and to indicate the closest equivalent.

There were 12 response options (plus 'no qualifications') covering professional and vocational qualifications, and a range of academic qualifications.

These are combined into five categories for the highest level of qualification, plus a category for no qualifications and one for other qualifications (which includes vocational or work-related qualifications, and for foreign qualifications where an equivalent qualification was not indicated):

• No Qualifications: No academic or professional qualifications
• Level 1 qualifications: 1-4 O Levels/CSE/GCSEs (any grades), Entry Level, Foundation Diploma, NVQ level 1, Foundation GNVQ, Basic/Essential Skills
• Level 2 qualifications: 5+ O Level (Passes)/CSEs (Grade 1)/GCSEs (Grades A*-C), School Certificate, 1 A Level/ 2-3 AS Levels/VCEs, Intermediate/Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First/General Diploma, RSA Diploma
• Apprenticeships
• Level 3 qualifications: 2+ A Levels/VCEs, 4+ AS Levels, Higher School Certificate, Progression/Advanced Diploma, Welsh Baccalaureate Advanced Diploma, NVQ Level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma
• Level 4+ qualifications: Degree (for example BA, BSc), Higher Degree (for example MA, PhD, PGCE), NVQ Level 4-5, HNC, HND, RSA Higher Diploma, BTEC Higher level, Foundation degree (NI), Professional qualifications (for example teaching, nursing, accountancy)
• Other qualifications: Vocational/Work-related Qualifications, Foreign Qualifications (Not stated/Level unknown)

NS-SeC (alternate classification)

The National Statistics Socio-economic Classification (NS-SEC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification. To assign a person to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed, and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. More information can be found on NS-SeC rebased on SOC2010.

The census did not ask a question about the number of employees at a person's workplace and as such, the reduced method of deriving NS-SeC (which does not require this information) is used. Note that the category 'L16: Occupation not stated or inadequately described' is not included in census tables because missing answers are imputed.

Abstract Objective The Robson 10 group classification system (RTGCS) is a reproducible, clinically relevant and prospective classification system proposed by the World Health Organization (WHO) as a global standard for assessing, monitoring and comparing cesarean section (CS) rates. The purpose of the present study is to analyze CS rates according to the RTGCS over a 3-year period and to identify the main contributors to this rate. Methods We reviewed data regarding deliveries performed from 2014 up to 2016 in a tertiary hospital in Portugal, and classified all women according to the RTGCS. We analyzed the CS rate in each group. Results We included data from 6,369 deliveries. Groups 1 (n = 1,703), 2 (n = 1,229) and 3 (n = 1,382) represented 67.7% of the obstetric population. The global CS rate was 25% (n = 1,594). Groups 1, 2, 5 and 10 were responsible for 74.2% of global CS deliveries. Conclusion As expected, Groups 1, 2, 5 and 10 were the greatest contributors to the overall CS rate. An attempt to increase the number of vaginal deliveries in these groups, especially in Groups 2 and 5, might contribute to the reduction of the CS rate.

Dataset population: Persons aged 16 and over

General health

General health is a self-assessment of a person's general state of health. People were asked to assess whether their health was very good, good, fair, bad or very bad.

For England and Wales, this assessment is not based on a person's health over any specified period of time.

NS-SeC

The National Statistics Socio-economic Classification (NS-SeC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification.

To assign a person aged 16 to 74 to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. Information about the classification is available here: NS-SEC rebased on SOC2010.

For 'Long-term unemployed', the year last worked is 2009 or earlier. In 2011 Census results, because the census did not ask a question about the number of employees at a person's workplace, the reduced method of deriving NS-SeC (which does not require this information) is used.

Dataset population: Persons aged 16 and over

NS-SeC

The National Statistics Socio-economic Classification (NS-SeC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification.

To assign a person aged 16 to 74 to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. Information about the classification is available here: NS-SEC rebased on SOC2010.

For 'Long-term unemployed', the year last worked is 2009 or earlier. In 2011 Census results, because the census did not ask a question about the number of employees at a person's workplace, the reduced method of deriving NS-SeC (which does not require this information) is used.

Religion

This is a person's current religion, or if the person does not have a religion, 'No religion'. No determination is made about whether a person was a practicing member of a religion. Unlike other census questions where missing answers are imputed, this question was voluntary and where no answer was provided, the response is categorised as 'Not stated'.

Context

Protein secondary structure prediction dataset. Used by 2015 NAR paper* from Barton group. There are a total of 1507 protein sequences, each represented by an integer identifier (e.g. 24695). 1348 in the training folder, and the rest in the blind test folder.

For each example, there are the following files: .fasta -> amino acid sequence for that domain .dssp -> ground truth 3-state secondary structures, obtained from PDB 3D crystal structures using the DSSP algorithm .pssm -> PSI-BLAST matrices, obtained from running the PSI-BLAST algorithm on the sequence, which returns both the matrix and a multiple-sequence alignment (MSA) .hmm -> profile HMM matrices, obtained by running the HMMer3 algorithm on the MSA generated from PSI-BLAST

The suggested k for cross validation is 7, such that each fold will have 193 (the last will have 190) protein sequences.

This leads on to the purpose of the third file in this dataset - shuffle.pkl. This file contains the suggested 7-fold split for cross-validation, in the form of a nested list. Random splits were generated until the 3-state secondary structure contents were within 1% of each other, to balance the prediction labels across the 7 folds.

*Alexey Drozdetskiy, Christian Cole, James Procter, Geoffrey J. Barton, JPred4: a protein secondary structure prediction server, Nucleic Acids Research, Volume 43, Issue W1, 1 July 2015, Pages W389–W394, https://doi.org/10.1093/nar/gkv332

Abstract

The dataset was derived by the Bioregional Assessment Programme. The parent dataset(s) is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

A raster of landform built using the Stein Index as described in the 'INTERIM CLASSIFICATION OF AQUATIC SYSTEMS IN THE MURRAY-DARLING BASIN' page 23-24.

This is used to classify streams according to whether it is upland or lowland relative to the surrounding terrain and high energy or low energy depending on landscape features.

Purpose

To provide a simple classification of streams for regions where there was limited information

Dataset History

The final definitions for landform, in terms of the Stein index are:

Lowland: mrVBF > 3;

Low Energy Upland: mrVBF <2.5 AND mrRTF >2.5;

High Energy Upland: mrVBF < 2.5 AND mrRTF <= 2.5;

Transitional: mrVBF >=2.5 AND mrVBF<=3.

The use of 2.5 and 3 as thresholds is based on the logic of expert opinion only applied to the scales represented in Table 9 with Lowland representing areas with a valley floor exceeding 90m wide. The resulting mapping was then examined and agreed upon by the Technical Advisory Group. Initial comparison with New South Wales River Styles mapping by New South Wales Office of Water for the Tenterfield Creek catchment in the north-eastern corner of New South Wales indicates the ANAE transitional category may be including a high number of streams the New South Wales River Styles program identified as 'upland'. This may indicate our thresholds need to be increased further (e.g. lowland >4, transitional 3.5 to 4). A visual inspection of other areas in New South Wales did not support changing thresholds at this time, but a more rigorous calibration and validation process has not yet been carried out. The New South Wales River Styles data set based on site observations is likely to be a valuable contributor to this process.

GIS application

The landform attribute was developed using the 3 sec mrVBF and mrRTF from CSIRO (Table 10)

Lowland: MrVBF_int > 3;

Low Energy Upland: MrVBF_int <= 2 AND MrRTF_int > 2;Lowland

High Energy Upland: MrVBF_int <= 2 AND MrRTF_int <= 2;

Transitional: MrVBF_int =3. (there are no values in MrVBF between 3 and 3.5, so this works)

Dataset Citation

Bioregional Assessment Programme (2015) Stein Index Classification for Streams National 20150513. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/8915d14d-7c22-404a-ba11-07f0c25fd177.

Dataset Ancestors

• Derived From Multi-resolution Valley Bottom Flatness MrVBF at three second resolution CSIRO 20000211

• Derived From Multi-resolution Ridge Top Flatness at 3 second resolution CSIRO 20000211

Lecture 1 — Machine Learning: Introduction to Machine Learning, Linear Models for Regression Reading: Bishop, Chapter 1: sec. 1.1 - 1.5. and Chapter 3: sec. 1.1 - 1.3. Optional: Bishop, Chapter 2: Backgorund material; Hastie, Tibshirani, Friedman, Chapters 2 and 3. Lecture 2 — Bayesian Framework: Bayesian Linear Regression, Evidence Maximization. Linear Models for Classification. Reading: Bishop, Chapter 3: sec. 3.3 - 3.5. Chapter 4. Optional: Radford Neal s NIPS tutorial on Bayesian Methods for Machine Learning:. Also see Max Welling s notes on Fisher Linear Discriminant Analysis Lecture 3 — Classification Linear Models for Classification, Generative and Discriminative approaches, Laplace Approximation. Reading: Bishop, Chapter 4. Optional: Hastie, Tibshirani, Friedman, Chapter 4. Lecture 4 — Graphical Models: Bayesian Networks, Markov Random Fields Reading: Bishop, Chapter 8. Optional: Hastie, Tibshirani, Friedman, Chapter 17 (Undirected Graphical Models). Mac

In Canada, natural health products and foods are regulated under the Food and Drugs Act (FDA) and its associated regulations. Products that meet the definition of a "natural health product" (NHP) as set out in the Natural Health Products Regulations (NHPR) are subject to the FDA as it applies to drugs and to the NHPR. Products that are foods as defined in the FDA are subject to the FDA as it applies to foods and to Parts A, B and D of the Food and Drug Regulations (FDR). It is important to note that the provisions of the FDR do not apply to products classified as NHPs except where such provisions are incorporated by reference into the NHPR, as per section 3 of the NHPR.

Dataset population: Persons aged 16 to 74

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

NS-SeC

The National Statistics Socio-economic Classification (NS-SeC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification.

To assign a person aged 16 to 74 to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. Information about the classification is available here: NS-SEC rebased on SOC2010.

For 'Long-term unemployed', the year last worked is 2009 or earlier. In 2011 Census results, because the census did not ask a question about the number of employees at a person's workplace, the reduced method of deriving NS-SeC (which does not require this information) is used.

Trends in selected fetal and newborn outcomes by Robson group.

Classification of institutional sectors and subsectors (CZ-CISS) according to ESA 2010 - level 3 - Sector III

This table contains 11685 series, with data for years 1997 - 2011 (not all combinations necessarily have data for all years), and was last released on 2013-05-15. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Newfoundland and Labrador; Canada; Nova Scotia; Prince Edward Island ...), Sector (3 items: Total economy; Non-business sector; Business sector ...), Labour productivity measures and related measures (15 items: Total number of jobs; Number of employee jobs; Number of self-employed jobs; Hours worked for all jobs ...), North American Industry Classification System (NAICS) (19 items: All industries; Agriculture; forestry; fishing and hunting ...).

Dataset population: Persons aged 16 and over

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

NS-SeC

The National Statistics Socio-economic Classification (NS-SeC) provides an indication of socio-economic position based on occupation. It is an Office for National Statistics standard classification.

To assign a person aged 16 to 74 to an NS-SeC category, their occupation title is combined with information about their employment status, whether they are employed or self-employed and whether or not they supervise other employees. Full-time students are recorded in the 'full-time students' category regardless of whether they are economically active or not.

The rebased version of NS-SeC used in census results uses occupation coded to SOC2010. Information about the classification is available here: NS-SEC rebased on SOC2010.

For 'Long-term unemployed', the year last worked is 2009 or earlier. In 2011 Census results, because the census did not ask a question about the number of employees at a person's workplace, the reduced method of deriving NS-SeC (which does not require this information) is used.