100+ datasets found
  1. Population Group Concepts and Types

    • johnsnowlabs.com
    csv
    Updated May 6, 2024
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    John Snow Labs (2024). Population Group Concepts and Types [Dataset]. https://www.johnsnowlabs.com/marketplace/population-group-concepts-and-types/
    Explore at:
    csvAvailable download formats
    Dataset updated
    May 6, 2024
    Dataset authored and provided by
    John Snow Labs
    Area covered
    N/A
    Description

    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.

  2. Population Growth per thousand inhabitants according to year

    • ine.es
    csv, html, json +4
    Updated Jun 24, 2024
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    INE - Instituto Nacional de Estadística (2024). Population Growth per thousand inhabitants according to year [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=36746&L=1
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    text/pc-axis, xlsx, txt, json, csv, html, xlsAvailable download formats
    Dataset updated
    Jun 24, 2024
    Dataset provided by
    Instituto Nacional de Estadísticahttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Jan 1, 2024 - Jan 1, 2038
    Variables measured
    Provinces, Type of data, Demographic Concepts
    Description

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

  3. Population Group Relationships

    • johnsnowlabs.com
    csv
    Updated May 6, 2024
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    John Snow Labs (2024). Population Group Relationships [Dataset]. https://www.johnsnowlabs.com/marketplace/population-group-relationships/
    Explore at:
    csvAvailable download formats
    Dataset updated
    May 6, 2024
    Dataset authored and provided by
    John Snow Labs
    Area covered
    N/A
    Description

    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.

  4. Immigration from abroad, by quarter and country of birth (top 3 countries)

    • ine.es
    csv, html, json +4
    Updated May 8, 2025
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    INE - Instituto Nacional de Estadística (2025). Immigration from abroad, by quarter and country of birth (top 3 countries) [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=59028&L=1
    Explore at:
    csv, text/pc-axis, txt, xlsx, html, json, xlsAvailable download formats
    Dataset updated
    May 8, 2025
    Dataset provided by
    National Statistics Institutehttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Apr 1, 2023 - Apr 1, 2025
    Variables measured
    Provinces, Country of birth, Demographic Concepts
    Description

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

  5. f

    Data from: A Conceptual Model of Probability for Determining Population...

    • scielo.figshare.com
    jpeg
    Updated Jun 2, 2023
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    Bruce Kelly da Nobrega Silva; Paulo Sergio Lucio; Fabrício Daniel dos Santos Silva; Madson Tavares Silva; Rafaela Lisboa Costa; Edivaldo Afonso de Oliveira Serrão; Vicente de Paulo Rodrigues da Silva; Rodrigo Lins da Rocha Júnior (2023). A Conceptual Model of Probability for Determining Population Vulnerability to Climate [Dataset]. http://doi.org/10.6084/m9.figshare.14282096.v1
    Explore at:
    jpegAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    SciELO journals
    Authors
    Bruce Kelly da Nobrega Silva; Paulo Sergio Lucio; Fabrício Daniel dos Santos Silva; Madson Tavares Silva; Rafaela Lisboa Costa; Edivaldo Afonso de Oliveira Serrão; Vicente de Paulo Rodrigues da Silva; Rodrigo Lins da Rocha Júnior
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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).

  6. W

    Migration Statistics Improvement Programme Reports

    • cloud.csiss.gmu.edu
    • data.wu.ac.at
    html
    Updated Dec 20, 2019
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    United Kingdom (2019). Migration Statistics Improvement Programme Reports [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/migration_statistics_improvement_programme_reports
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    htmlAvailable download formats
    Dataset updated
    Dec 20, 2019
    Dataset provided by
    United Kingdom
    License

    http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence

    Description

    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

  7. f

    DataSheet1_Ancestry: How researchers use it and what they mean by it.PDF

    • figshare.com
    pdf
    Updated May 31, 2023
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    Bege Dauda; Santiago J. Molina; Danielle S. Allen; Agustin Fuentes; Nayanika Ghosh; Madelyn Mauro; Benjamin M. Neale; Aaron Panofsky; Mashaal Sohail; Sarah R. Zhang; Anna C. F. Lewis (2023). DataSheet1_Ancestry: How researchers use it and what they mean by it.PDF [Dataset]. http://doi.org/10.3389/fgene.2023.1044555.s001
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Bege Dauda; Santiago J. Molina; Danielle S. Allen; Agustin Fuentes; Nayanika Ghosh; Madelyn Mauro; Benjamin M. Neale; Aaron Panofsky; Mashaal Sohail; Sarah R. Zhang; Anna C. F. Lewis
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  8. [Dataset] Data for the course "Population Genomics" at Aarhus University

    • zenodo.org
    application/gzip, bin
    Updated Jan 8, 2025
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    Samuele Soraggi; Samuele Soraggi; Kasper Munch; Kasper Munch (2025). [Dataset] Data for the course "Population Genomics" at Aarhus University [Dataset]. http://doi.org/10.5281/zenodo.7670839
    Explore at:
    application/gzip, binAvailable download formats
    Dataset updated
    Jan 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Samuele Soraggi; Samuele Soraggi; Kasper Munch; Kasper Munch
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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:
    2. Drift and the coalescent:
    3. Recombination:
    4. Population strucure and incomplete lineage sorting:
    5. Hidden Markov models:
    6. Ancestral recombination graphs:
    7. Past population demography:
    8. Direct and linked selection:
    9. Admixture:
    10. Genome-wide association study (GWAS):
    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
  9. e

    Statistical Measuring Concepts of Historical Population Evolution (ISTAC:...

    • data.europa.eu
    unknown
    Updated Sep 21, 2023
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    Comunidad Autónoma de Canarias (2023). Statistical Measuring Concepts of Historical Population Evolution (ISTAC: CSM_C00025A) [Dataset]. https://data.europa.eu/data/datasets/https-datos-canarias-es-catalogos-estadisticas-dataset-urn-sdmx-org-sdmx-infomodel-conceptscheme-conceptscheme-istac-csm_c00025a?locale=en
    Explore at:
    unknownAvailable download formats
    Dataset updated
    Sep 21, 2023
    Dataset authored and provided by
    Comunidad Autónoma de Canarias
    License

    http://www.gobiernodecanarias.org/istac/aviso_legal.htmlhttp://www.gobiernodecanarias.org/istac/aviso_legal.html

    Description

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

  10. Emigration abroad, by quarter and country of birth (top 3 countries)

    • ine.es
    csv, html, json +4
    Updated May 8, 2025
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    INE - Instituto Nacional de Estadística (2025). Emigration abroad, by quarter and country of birth (top 3 countries) [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=59029&L=1
    Explore at:
    csv, txt, text/pc-axis, json, html, xlsx, xlsAvailable download formats
    Dataset updated
    May 8, 2025
    Dataset provided by
    National Statistics Institutehttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Apr 1, 2023 - Apr 1, 2025
    Variables measured
    Provinces, Country of birth, Demographic Concepts
    Description

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

  11. a

    Population dynamics

    • geoinquiries-education.hub.arcgis.com
    Updated Aug 11, 2021
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    Esri GIS Education (2021). Population dynamics [Dataset]. https://geoinquiries-education.hub.arcgis.com/documents/534570d4a813435d8fcdf964730bacd5
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    Dataset updated
    Aug 11, 2021
    Dataset authored and provided by
    Esri GIS Education
    Description

    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

  12. f

    Theoretical concepts and definitions of study domains and variables.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Stacy A. Drake; Yijiong Yang; Dwayne A. Wolf; Thomas Reynolds; Sherhonda Harper; Antoinette Hudson; Janet C. Meininger (2023). Theoretical concepts and definitions of study domains and variables. [Dataset]. http://doi.org/10.1371/journal.pone.0212026.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Stacy A. Drake; Yijiong Yang; Dwayne A. Wolf; Thomas Reynolds; Sherhonda Harper; Antoinette Hudson; Janet C. Meininger
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Theoretical concepts and definitions of study domains and variables.

  13. Resident population by date, sex and age

    • ine.es
    csv, html, json +4
    Updated May 8, 2025
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    INE - Instituto Nacional de Estadística (2025). Resident population by date, sex and age [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=59589&L=1
    Explore at:
    txt, csv, xlsx, json, text/pc-axis, html, xlsAvailable download formats
    Dataset updated
    May 8, 2025
    Dataset provided by
    National Statistics Institutehttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Apr 1, 2024 - Apr 1, 2025
    Variables measured
    Sex, Provinces, Simple age, Type of data, Demographic Concepts
    Description

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

  14. g

    Operational concepts of General Statistics of the Prison Population (ISTAC:...

    • gimi9.com
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    Operational concepts of General Statistics of the Prison Population (ISTAC: CSO E68020A) | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_887e2f1f2c6eb24caa58746f37bbcb8e0484b8c5/
    Explore at:
    Description

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

  15. b

    Area Master Plan

    • data.baltimorecity.gov
    Updated Nov 23, 2020
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    Baltimore City (2020). Area Master Plan [Dataset]. https://data.baltimorecity.gov/datasets/area-master-plan?geometry=-76.762%2C39.200%2C-76.479%2C39.386
    Explore at:
    Dataset updated
    Nov 23, 2020
    Dataset authored and provided by
    Baltimore City
    Area covered
    Description

    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.

  16. b

    Area Master Plan

    • data.baltimorecity.gov
    Updated May 25, 2023
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    Baltimore City (2023). Area Master Plan [Dataset]. https://data.baltimorecity.gov/maps/baltimore::area-master-plan-1/about
    Explore at:
    Dataset updated
    May 25, 2023
    Dataset authored and provided by
    Baltimore City
    Area covered
    Description

    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.

  17. g

    Population aged 16 and over according to lack in certain concepts and...

    • gimi9.com
    Updated Nov 7, 2023
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    (2023). Population aged 16 and over according to lack in certain concepts and relationship with the activity. Canary Islands. 2018 | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_d7d08926804525cb92b6c8ac905be0f3ac8ed57f/
    Explore at:
    Dataset updated
    Nov 7, 2023
    Area covered
    Canary Islands
    Description

    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.

  18. g

    Total population according to lack in certain concepts. Canary Islands. 2022...

    • gimi9.com
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    Total population according to lack in certain concepts. Canary Islands. 2022 | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_0f7d610d5f3d574590d9df5085d4a5a25150769c/
    Explore at:
    Area covered
    Canary Islands
    Description

    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.

  19. g

    Measuring Concepts of Official Population Figures (ISTAC: CSM E30245A) |...

    • gimi9.com
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    Measuring Concepts of Official Population Figures (ISTAC: CSM E30245A) | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_ab6d1c75117af9dbf2ddbe03e488f5acef246861
    Explore at:
    Description

    🇪🇸 스페인

  20. Z

    Data for the course "Population Genomics" at Aarhus University

    • data.niaid.nih.gov
    • zenodo.org
    Updated Mar 9, 2023
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    Kasper Munch (2023). Data for the course "Population Genomics" at Aarhus University [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7551293
    Explore at:
    Dataset updated
    Mar 9, 2023
    Dataset provided by
    Kasper Munch
    Samuele Soraggi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Aarhus
    Description

    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

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John Snow Labs (2024). Population Group Concepts and Types [Dataset]. https://www.johnsnowlabs.com/marketplace/population-group-concepts-and-types/
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Population Group Concepts and Types

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Dataset updated
May 6, 2024
Dataset authored and provided by
John Snow Labs
Area covered
N/A
Description

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.

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