9 datasets found
  1. TONS (Training Online Nomination System) Training Master File

    • catalog.data.gov
    • data.amerigeoss.org
    Updated Jul 4, 2025
    + more versions
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    Social Security Administration (2025). TONS (Training Online Nomination System) Training Master File [Dataset]. https://catalog.data.gov/dataset/tons-training-online-nomination-system-training-master-file
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    A file that holds the master records for all online training courses nominated for reimbursement.

  2. LinkedIn: U.S. users 2025, by education

    • statista.com
    Updated Jun 25, 2025
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    Statista (2025). LinkedIn: U.S. users 2025, by education [Dataset]. https://www.statista.com/statistics/246180/share-of-us-internet-users-who-use-linkedin-by-education-level/
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    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 2023
    Area covered
    United States
    Description

    According to an online survey conducted in February 2025 in the United States, ********* of LinkedIn users held a bachelor degree or equivalent. Additionally, ** percent of LinkedIn users in the U.S. held a masters degree or equivalent.

  3. 4

    Unpacking Dresden, data underlying the MSc research project: Applied Spatial...

    • data.4tu.nl
    zip
    Updated Jun 26, 2025
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    Alexandre Bry; Adriano Mancini; Alankrita Sharma; Grase Stephanie Stuka; Soroush Saffarzadeh (2025). Unpacking Dresden, data underlying the MSc research project: Applied Spatial Analytics for Sustainable Urban Development [Dataset]. http://doi.org/10.4121/48e04672-93f4-49a4-9c7b-76c57a844e24.v1
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    zipAvailable download formats
    Dataset updated
    Jun 26, 2025
    Dataset provided by
    4TU.ResearchData
    Authors
    Alexandre Bry; Adriano Mancini; Alankrita Sharma; Grase Stephanie Stuka; Soroush Saffarzadeh
    License

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

    Area covered
    Description

    More information about the context and the methodology can be found in the README.md file and online at this link: https://github.com/sdgis-edu-tud/fair-data-publication-groupf.


    Along with the Elbe river, Dresden comprises a dense network of streams, which are spread out across its fabric. Presently, the streams are secluded from being a valuable part of the city. The problems are characterised by ecological issues, inappropriate land use by residents, and artificial channeling. They, along with the Elbe river hold potential to become elements of integrating the ecological and social functions of the city by reclaiming the historical identity of waterfronts and restoring natural habitats. Therefore, there arises a need to understand how to integrate these streams into the network of protected green areas and public spaces, while maximising their contribution to biodiversity while adapting to the risk of flooding within and around the city.


    These concerns and identified potentials beg the question that, how can urban streams be restored and integrated in Dresden's fabric, such that there is a synergy between human activities and the natural environment?


    This is investigated by adopting an integrated approach for biodiversity, climate adaptation and quality of life.


    Based on the three criteria that we decided to tackle, we came up with numerical indicators that we could use to evaluate them. These numerical indicators are called attributes and have to be normalised—in our case between 0 and 1—so that they can be compared, weighted and thereafter clustered properly depending on their relevance and similarities.


    The spatial units used in this study are hexagons with a dimension of 250 meters. The study area of Dresden is divided using a complete surface of a hexagonal pattern. Then it is overlaid with the water stream network and river body from OpenStreetMap to keep only the hexagons that intersect with at least one stream. Finally, the isolated hexagons were removed.


    Two data-driven methods were used to conduct the analysis:


    • S-MCDA (Spatial Multi-Criteria Decision Analysis) — S-MCDA was used to weigh the different attributes against each other. The method supports decision-making by evaluating and ranking alternatives (the attributes) within the three objectives of biodiversity, climate adaptation and quality of life.
    • Typology Construction — Typology construction is used to group attributes into homogenous types based on similarities. This was used to identify patterns in data and make clusters of attributes that show similarity, which can thereafter be used to understand the type of interventions which would be impactful.


    This dataset contains both the values computed for the attributes in each spatial unit and the final results of the two methods.

  4. A

    Massive Open Online Course (MOOC) Market Study by Reskilling & Online...

    • factmr.com
    csv, pdf
    Updated May 7, 2024
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    Fact.MR (2024). Massive Open Online Course (MOOC) Market Study by Reskilling & Online Certification, Language & Casual Learning, Supplemental Education, Higher Education, and Test Preparation from 2024 to 2034 [Dataset]. https://www.factmr.com/report/3077/mooc-market
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    csv, pdfAvailable download formats
    Dataset updated
    May 7, 2024
    Dataset provided by
    Fact.MR
    License

    https://www.factmr.com/privacy-policyhttps://www.factmr.com/privacy-policy

    Time period covered
    2024 - 2034
    Area covered
    Worldwide
    Description

    The global massive open online course (MOOC) market size is calculated to advance at a CAGR of 32% through 2034, which is set to increase its market value from US$ 13.2 billion in 2024 to US$ 212.7 billion by the end of 2034.

    Report AttributeDetail
    MOOC Market Size (2024E)US$ 13.2 Billion
    Projected Market Value (2034F)US$ 212.7 Billion
    Global Market Growth Rate (2024 to 2034)32% CAGR
    China Market Value (2034F)US$ 23.3 Billion
    Japan Market Growth Rate (2024 to 2034)32.6% CAGR
    North America Market Share (2024E)23.9%
    East Asia Market Value (2034F)US$ 49.1 Billion
    Key Companies Profiled

    Alison; Coursera Inc; edX Inc; Federica.EU; FutureLearn; Instructure; Intellipaat; iverity; Jigsaw Academy; Kadenze.

    Country Wise Insights

    AttributeUnited States
    Market Value (2024E)US$ 1.4 Billion
    Growth Rate (2024 to 2034)32.5% CAGR
    Projected Value (2034F)US$ 23.6 Billion
    AttributeChina
    Market Value (2024E)US$ 1.5 Billion
    Growth Rate (2024 to 2034)32% CAGR
    Projected Value (2034F)US$ 23.3 Billion

    Category-wise Insights

    AttributexMOOC
    Segment Value (2024E)US$ 9.3 Billion
    Growth Rate (2024 to 2034)30.8% CAGR
    Projected Value (2034F)US$ 136.1 Billion
    AttributeDegree & Master Programs
    Segment Value (2024E)US$ 6.4 Billion
    Growth Rate (2024 to 2034)30.2% CAGR
    Projected Value (2034F)US$ 89.3 Billion
  5. Data Science related tracks

    • kaggle.com
    Updated Jun 12, 2021
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    swdmop (2021). Data Science related tracks [Dataset]. https://www.kaggle.com/andrsenrique/data-science-online-specializations-dataset/metadata
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 12, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    swdmop
    License

    http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

    Description

    Dataset

    This dataset was created by swdmop

    Released under GPL 2

    Contents

  6. f

    Descriptive statistics-aggregate data.

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
    + more versions
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    Claudiu Coman; Luiza Mesesan-Schmitz; Laurentiu Gabriel Tiru; Gabriela Grosseck; Maria Cristina Bularca (2023). Descriptive statistics-aggregate data. [Dataset]. http://doi.org/10.1371/journal.pone.0257729.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Claudiu Coman; Luiza Mesesan-Schmitz; Laurentiu Gabriel Tiru; Gabriela Grosseck; Maria Cristina Bularca
    License

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

    Description

    Descriptive statistics-aggregate data.

  7. Most common places to see the MSC Marine Ecolabel in Japan 2023

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Most common places to see the MSC Marine Ecolabel in Japan 2023 [Dataset]. https://www.statista.com/statistics/1416437/japan-most-common-places-to-see-the-msc-marine-ecolabel/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Japan
    Description

    In a survey conducted in February 2023 in Japan regarding the most common places to see the MSC Marine Ecolabel, majority of the respondents has stated that they have seen it at the supermarket which accounted for approximately ** percent. Moreover, around ** percent of the respondents has stated that they have seen it on the internet.

  8. m

    Master OP EXP VIEW

    • opendata.montgomeryal.gov
    • citymgm.hub.arcgis.com
    • +2more
    Updated Jan 27, 2020
    + more versions
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    City of Montgomery ArcGIS Online (2020). Master OP EXP VIEW [Dataset]. https://opendata.montgomeryal.gov/datasets/CityMGM::master-op-exp-view/about
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    Dataset updated
    Jan 27, 2020
    Dataset authored and provided by
    City of Montgomery ArcGIS Online
    Area covered
    Description

    Table View of Master_OP_EXP - Budgets and Actuals from FY 2016, 2017, 2018, 2019, and FYTD 2020. This View is the data source for Expense Dashboards. Update Schedule: Once per Month.

  9. f

    Data from: Graduates from a Professional Master’s Degree Program in Family...

    • scielo.figshare.com
    xls
    Updated Jun 1, 2023
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    Rocio Fernandes Santos Viniegra; Luis Guilherme Pessoa da Silva; Adriana Cavalcanti de Aguiar; Luciana Souza (2023). Graduates from a Professional Master’s Degree Program in Family Health: Expectations, Motivations and Benefits [Dataset]. http://doi.org/10.6084/m9.figshare.9985946.v1
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    Rocio Fernandes Santos Viniegra; Luis Guilherme Pessoa da Silva; Adriana Cavalcanti de Aguiar; Luciana Souza
    License

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

    Description

    ABSTRACT The health care model based on the Family Health Strategy, created in the early 1990s, encouraged changes in health education, highlighting the need to create lato and stricto sensu postgraduate courses aimed at empowering professionals that foster comprehensive health care. Periodic evaluations are carried out and encouraged by Capes/MEC in order to maintain the quality of postgraduate courses, but evaluations of recently-introduced professional master’s degree courses in family health remain scarce. Objectives To describe the academic profile, contribution, motivations and expectations of graduates of a Professional Master’s in Family Health. Method Cross-sectional and quantitative study to analyze the results of 102 questionnaires answered by graduates of the Professional Master’s Degree in Family Health of the Estácio de Sá University (RJ), who had concluded the course between 2007 and 2012. The instrument consisted of open-ended and closed-ended questions, sent by e-mail and made available online through the electronic platform Survey Monkey. The study evaluated age, gender, regional origin, academic background, as well as the contributions, expectations and motivations related to the course. Results The survey sample was formed predominantly by female graduates, aged over 30, from 13 Brazilian states and, mainly from Medicine and Nursing courses. The contribution of the master’s degree to the graduate’s professional life was evaluated as excellent by 77% of the interviewees. The expectations regarding the course were positively evaluated and the main reasons for seeking the qualification were scientific-technical improvement and personal satisfaction, rather than better salaries or job stability. Conclusion The course was evaluated positively by the graduates, having exceeded their expectations and satisfied the interests that led them to it, thus producing changes to their personal and professional life. A longitudinal analysis of the impact of the professional master’s degree in the career of graduates will require a sequence of similar studies, as has been stimulated by Capes/MEC in recent years.

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    Learn how you can add new datasets to our index.

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Social Security Administration (2025). TONS (Training Online Nomination System) Training Master File [Dataset]. https://catalog.data.gov/dataset/tons-training-online-nomination-system-training-master-file
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TONS (Training Online Nomination System) Training Master File

Explore at:
Dataset updated
Jul 4, 2025
Dataset provided by
Social Security Administrationhttp://ssa.gov/
Description

A file that holds the master records for all online training courses nominated for reimbursement.

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