This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Population Group". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

Population Projections: Population Growth per thousand inhabitants according to year. Annual. Provinces.

This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Population Group". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.

Continuous Population Statistics: Immigration from abroad, by quarter and country of birth (top 3 countries). Quarterly. Provinces.

Abstract The population's vulnerability assessment is an analysis of the expected impacts, risk modeling, exposure, sensitivity and lack of adaptability of a specific region or sector to the effects of extreme weather events. The vulnerability encompasses a variety of concepts including sensitivity or susceptibility to harm and a lack of ability to cope and adapt. One way to analyze these very different aspects is through a stochastic process, such as conditional probability. In this article, a conceptual model is presented to assess the population's vulnerability to the climate, taking into account one of the susceptible regions of Brazil, the Northeast. The results show that the proposed indicator IVPopS presents the most vulnerable central semi-arid region, in addition to a detailed assessment in each component of the indicator. Production areas such as Petrolina-PE have high levels of risk (0,604), exposure (0,863), sensitivity (0,910), while the inability to adapt is low (0.002).

The Migration Statistics Improvement Programme formally closes on 31 March 2012. Five reports are being published:

Migration Statistics Improvement Programme Final Report

A Conceptual Framework for Population and Migration Statistics

Research report: Using administrative data to set plausibility ranges for population estimates in England and Wales

Research Report: Uncertainty in Local Authority Mid Year Population Estimates

Strategy for Delivering Statistical Benefits from e-Borders

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Migration Statistics Improvement Programme Reports

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered.Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles.Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups.Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

Outline of measurement concepts for the publication of the Historical Evolution of the Population in the censuses.

Continuous Population Statistics: Emigration abroad, by quarter and country of birth (top 3 countries). Quarterly. Provinces.

This activity uses Map Viewer. ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsAPES: III. B. – Population biology concepts.APES: II.B.1. – Human population dynamics - historical population sizes; distribution; fertility rates; growth rates and doubling times; demographic transition; age-structure diagrams.Learning outcomesStudents will predict total historical population trends from age-structure information.Students will relate population growth to k (carrying capacity) or r (reproductive factor) selective environmental conditions.More activitiesAll Environmental Science GeoInquiriesAll GeoInquiries

Theoretical concepts and definitions of study domains and variables.

Continuous Population Statistics: Resident population by date, sex and age. Quarterly. Provinces.

Outline of operational concepts for the publication of General Statistics of the Prison Population data

This dataset represent the Area Master Plan which is a dynamic and long-term planning document that provides a conceptual layout to guide future growth and development.

This dataset represents the Area Master Plan which is a dynamic and long-term planning document that provides a conceptual layout to guide future growth and development To leave feedback or ask a question about this dataset, please fill out the following form: Area Master Plan feedback form.

This table provides data for 2018 on the estimated population aged 16 and over in the Canary Islands due to lack of certain concepts and relationship with activity.

This table provides data for the year 2022 on the total population estimated by lack in certain concepts. The information is disaggregated territorially at the level of Canary Islands.

🇪🇸 스페인

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch.

Data.tar.gz Contains the datasets and executable files for some of the softwares

Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes. You do this in the command line by

creating the folder Course_Env: mkdir Course_Env

untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env

Activate the environment: conda activate ./Course_Env

Run the unpacking script (it can take quite some time to get it done): conda-unpack

Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the versione above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.

environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:

conda env create -f environment_with_args.yml -p ./Course_Env

conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

Identify an experimental platform relevant to a population genomic analysis.

Apply commonly used population genomic methods.

Explain the theory behind common population genomic methods.

Reflect on strengths and limitations of population genomic methods.

Interpret and analyze results of population genomic inference.

Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

Course intro and overview:

Coop chapters 1, 2, 3, Paper: Genome Diversity Project

Drift and the coalescent:

Coop chapter 4; Paper: Platypus

Exercise: Read mapping and base calling

Recombination:

Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation

Exercise: Phasing and recombination rate

Population strucure and incomplete lineage sorting:

Lecture: Coop chapter 6, Review: Incomplete lineage sorting

Exercise: Working with VCF files

Hidden Markov models:

Lecture: Durbin chapter 3, Paper: population structure

Exercise: Inference of population structure and admixture

Ancestral recombination graphs:

Lecture: Paper: Approximating the ARG, Paper: Tree inference

Exercise: ARG dashboard exercises + Inference of trees along sequence

Past population demography:

Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference

Exercise: Inferring historical populations

Direct and linked selection:

Lecture: Coop chapters 12, 13, revisit Paper: Tree inference

Admixture:

Lecture: Review: Admixture, Paper: Admixture inference

Exercise: Detecting archaic ancestry in modern humans

Genome-wide association study (GWAS):

Lecture: Coop lecture notes 99-120

Exercise: GWAS quality control

Heritability:

Lecture: Missing heritability and mixed models review ; Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)

Exercise: Association testing

Evolution and disease:

Lecture: Genetic architecture review ; Article about "omnigenic" model ; Coop Lecture notes Sec. 11.0.1 (p217-221)

Exercise: Estimating heritability