100+ datasets found
  1. f

    Breakdown of demographics from the SET dataset by faculty.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston (2023). Breakdown of demographics from the SET dataset by faculty. [Dataset]. http://doi.org/10.1371/journal.pone.0209749.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston
    License

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

    Description

    Across the rows are: total number of individual student surveys; total number of unique courses; number of female teachers with non-English and English speaking background; number of male teachers with non-English (NE) and English (E) speaking background; and the number of female and male international (I) and local (L) students.

  2. h

    Post-fit data/background

    • hepdata.net
    Updated Apr 19, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2023). Post-fit data/background [Dataset]. http://doi.org/10.17182/hepdata.135387.v2/t2
    Explore at:
    Dataset updated
    Apr 19, 2023
    Description

    Distribution of the BDT classifier response for data and for the expected SM background after the background-only fit. The expectations...

  3. S

    Post-fit data/background in ee channel

    • hepdata.net
    csv +3
    Updated 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    HEPData (2022). Post-fit data/background in ee channel [Dataset]. http://doi.org/10.17182/hepdata.135387.v1/t8
    Explore at:
    csv, https://root.cern, https://yoda.hepforge.org, https://yaml.orgAvailable download formats
    Dataset updated
    2022
    Dataset provided by
    HEPData
    Description

    Distribution of the BDT classifier response in data and for the expected SM background after the background-only fit, in the...

  4. f

    Number of sources by bin number.

    • plos.figshare.com
    xls
    Updated Jun 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Joseph Friedman; Nicholas Graetz; Emmanuela Gakidou (2023). Number of sources by bin number. [Dataset]. http://doi.org/10.1371/journal.pone.0208019.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 17, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Joseph Friedman; Nicholas Graetz; Emmanuela Gakidou
    License

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

    Description

    Number of sources by bin number.

  5. Background information

    • figshare.com
    docx
    Updated Feb 3, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Flavia Sharlet Noronha; TESSY JOSE; ANICE GEORGE; Linu Sara George (2022). Background information [Dataset]. http://doi.org/10.6084/m9.figshare.19114418.v1
    Explore at:
    docxAvailable download formats
    Dataset updated
    Feb 3, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Flavia Sharlet Noronha; TESSY JOSE; ANICE GEORGE; Linu Sara George
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This datasheet will collect background information of the participants for the study Effectiveness of Gatekeeper Training Program (GTP) on awareness, attitude, mental help seeking intention and gatekeeper behavior among Koraga tribe: A study protocol

  6. S

    Screening Software for Background Checks Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Apr 29, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Data Insights Market (2025). Screening Software for Background Checks Report [Dataset]. https://www.datainsightsmarket.com/reports/screening-software-for-background-checks-1943778
    Explore at:
    doc, ppt, pdfAvailable download formats
    Dataset updated
    Apr 29, 2025
    Dataset authored and provided by
    Data Insights Market
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global market for screening software for background checks is experiencing robust growth, driven by increasing concerns about workplace safety and regulatory compliance across various industries. The rising adoption of cloud-based solutions, offering scalability and cost-effectiveness, further fuels this expansion. While precise market sizing data is unavailable, a logical estimation based on industry trends and comparable markets suggests a current market valuation in the billions of dollars, with a Compound Annual Growth Rate (CAGR) projected between 10% and 15% for the forecast period (2025-2033). This growth is propelled by several factors: the increasing need for efficient and thorough background checks to mitigate risks associated with hiring unsuitable candidates, the growing awareness of potential legal liabilities related to negligent hiring, and the continuous evolution of technologies that enhance the accuracy and speed of background screening processes. The market is segmented by application (SMEs and large enterprises) and deployment type (cloud-based and on-premises), with cloud-based solutions gaining significant traction due to their accessibility and flexible pricing models. Large enterprises, with their stricter compliance requirements and larger workforce, represent a considerable portion of the market, driving demand for sophisticated, integrated screening solutions. Geographic regions like North America and Europe currently dominate the market, fueled by stringent regulations and higher adoption rates, but significant growth potential exists in emerging markets in Asia-Pacific and Middle East & Africa as awareness of best hiring practices increases. However, challenges such as data privacy concerns, escalating costs associated with comprehensive background checks, and the complexities of navigating diverse global regulations pose restraints to market expansion. The competitive landscape is dynamic, with several established players and emerging startups offering a diverse range of solutions catering to specific needs and budgets. The key players compete based on features, pricing, compliance certifications, and integration capabilities with existing HR systems. Future growth will likely be shaped by innovation in areas such as AI-powered candidate screening, improved data analytics for risk assessment, and the integration of background checks with broader HR technology platforms. Furthermore, the market will see increased demand for solutions addressing evolving legal requirements and data security standards, creating opportunities for vendors to differentiate their offerings and capitalize on the ongoing growth of this essential sector.

  7. d

    Personnel Hiring Data (WTTS/EODS) and Recruitment Data (USAJobs) -.

    • datadiscoverystudio.org
    Updated Mar 1, 2017
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2017). Personnel Hiring Data (WTTS/EODS) and Recruitment Data (USAJobs) -. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/c45cae728b284947a69386b862c35fd2/html
    Explore at:
    Dataset updated
    Mar 1, 2017
    Description

    description: This data set contains personnel data for DOT new hires and recruits. This data is maintained by the current HR and payroll provider (Department of Interior's IBC) and USAJobs. The data contains PII (Employee Name, SSN, Date of Birth, Home Address, etc.), Civil Rights (Disability, Gender, Race) and other sensitive data (Background Investigations and Security Clearance).; abstract: This data set contains personnel data for DOT new hires and recruits. This data is maintained by the current HR and payroll provider (Department of Interior's IBC) and USAJobs. The data contains PII (Employee Name, SSN, Date of Birth, Home Address, etc.), Civil Rights (Disability, Gender, Race) and other sensitive data (Background Investigations and Security Clearance).

  8. P-values for the effect size of the interaction terms, for different...

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston (2023). P-values for the effect size of the interaction terms, for different faculties. [Dataset]. http://doi.org/10.1371/journal.pone.0209749.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston
    License

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

    Description

    Significant terms (at 5% level) are highlighted in bold font.

  9. f

    Relative odds or effect size for different teacher/student populations.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston (2023). Relative odds or effect size for different teacher/student populations. [Dataset]. http://doi.org/10.1371/journal.pone.0209749.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston
    License

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

    Description

    Columns indicate student attribute and rows indicate teacher attribute. Confidence intervals are given in brackets, and significant (at 5% level) terms are highlighted in bold font. Confidence intervals not including the value 1 indicates significance.

  10. AUC values with 95% bootstrapped confidence intervals assess how well the...

    • plos.figshare.com
    xls
    Updated Jun 6, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston (2023). AUC values with 95% bootstrapped confidence intervals assess how well the model can discriminate SET scores ≤ 1, …, 5. [Dataset]. http://doi.org/10.1371/journal.pone.0209749.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston
    License

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

    Description

    Values between 0.7-0.8 are generally considered good, 0.8-0.9 is considered excellent whilst 0.9-1 is considered outstanding. The models are excellent at discriminating high SET scores (5-6) from low SET scores (≤ 4), with AUC’s between 0.96–0.99, and good at discriminating very high (6) from SET scores ≤ 5, with AUC’s 0.79-0.89.

  11. Predictive validity of tested models.

    • plos.figshare.com
    xls
    Updated May 30, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Joseph Friedman; Nicholas Graetz; Emmanuela Gakidou (2023). Predictive validity of tested models. [Dataset]. http://doi.org/10.1371/journal.pone.0208019.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Joseph Friedman; Nicholas Graetz; Emmanuela Gakidou
    License

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

    Description

    Predictive validity of tested models.

  12. f

    Percentage of cases and controls reporting symptoms or statuses within the...

    • figshare.com
    xls
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ananya Malhotra; Bernard Rachet; Audrey Bonaventure; Stephen P. Pereira; Laura M. Woods (2023). Percentage of cases and controls reporting symptoms or statuses within the 24-month period prior to diagnosis. [Dataset]. http://doi.org/10.1371/journal.pone.0251876.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ananya Malhotra; Bernard Rachet; Audrey Bonaventure; Stephen P. Pereira; Laura M. Woods
    License

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

    Description

    Percentage of cases and controls reporting symptoms or statuses within the 24-month period prior to diagnosis.

  13. f

    dataset.xlsx

    • figshare.com
    xlsx
    Updated Jan 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    K Baishnobi Patro (2025). dataset.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.28294460.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jan 28, 2025
    Dataset provided by
    figshare
    Authors
    K Baishnobi Patro
    License

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

    Description

    It is a panel dataset that includes currency and banking crises dataset for 32 emerging economies along with data for some macroeconomic variables.

  14. Demographic information of sampled patients.

    • plos.figshare.com
    xls
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Yunyu Huang; Pepijn Vemer; Jingjing Zhu; Maarten J. Postma; Wen Chen (2023). Demographic information of sampled patients. [Dataset]. http://doi.org/10.1371/journal.pone.0159297.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Yunyu Huang; Pepijn Vemer; Jingjing Zhu; Maarten J. Postma; Wen Chen
    License

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

    Description

    Demographic information of sampled patients.

  15. f

    Transportation assimilation: The immigrant sample.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dafeng Xu (2023). Transportation assimilation: The immigrant sample. [Dataset]. http://doi.org/10.1371/journal.pone.0194296.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Dafeng Xu
    License

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

    Description

    Transportation assimilation: The immigrant sample.

  16. f

    Descriptive characteristics of the districts in our dataset.

    • plos.figshare.com
    xls
    Updated Jun 10, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Grace Guan; Yotam Dery; Matan Yechezkel; Irad Ben-Gal; Dan Yamin; Margaret L. Brandeau (2023). Descriptive characteristics of the districts in our dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0253865.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Grace Guan; Yotam Dery; Matan Yechezkel; Irad Ben-Gal; Dan Yamin; Margaret L. Brandeau
    License

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

    Description

    Descriptive characteristics of the districts in our dataset.

  17. Average annual costs per patient (USD).

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Yunyu Huang; Pepijn Vemer; Jingjing Zhu; Maarten J. Postma; Wen Chen (2023). Average annual costs per patient (USD). [Dataset]. http://doi.org/10.1371/journal.pone.0159297.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Yunyu Huang; Pepijn Vemer; Jingjing Zhu; Maarten J. Postma; Wen Chen
    License

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

    Description

    Average annual costs per patient (USD).

  18. f

    Number of user profiles in Germany stratified by age group as retrieved by...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stefan Michael Scholz; Oliver Damm; Svenja Elkenkamp; Ulrich Marcus; Wolfgang Greiner; Axel Jeremias Schmidt (2023). Number of user profiles in Germany stratified by age group as retrieved by the search engine. [Dataset]. http://doi.org/10.1371/journal.pone.0212175.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Stefan Michael Scholz; Oliver Damm; Svenja Elkenkamp; Ulrich Marcus; Wolfgang Greiner; Axel Jeremias Schmidt
    License

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

    Area covered
    Germany
    Description

    Due to limitations of the search engine, age groups are overlapping and thus the total number of profiles is higher than the (correct) total of profiles.

  19. f

    Transportation assimilation: Length of stay less than 10 years.

    • plos.figshare.com
    • figshare.com
    xls
    Updated May 30, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dafeng Xu (2023). Transportation assimilation: Length of stay less than 10 years. [Dataset]. http://doi.org/10.1371/journal.pone.0194296.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Dafeng Xu
    License

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

    Description

    Transportation assimilation: Length of stay less than 10 years.

  20. f

    Agreement of VA functional status data with the reference standard of...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Rebecca T. Brown; Kiya D. Komaiko; Ying Shi; Kathy Z. Fung; W. John Boscardin; Alvin Au-Yeung; Gary Tarasovsky; Riya Jacob; Michael A. Steinman (2023). Agreement of VA functional status data with the reference standard of research-collected data for assessing dependence in activities of daily living, among patients with 2–4 weeks elapsed between assessments.a [Dataset]. http://doi.org/10.1371/journal.pone.0178726.t007
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Rebecca T. Brown; Kiya D. Komaiko; Ying Shi; Kathy Z. Fung; W. John Boscardin; Alvin Au-Yeung; Gary Tarasovsky; Riya Jacob; Michael A. Steinman
    License

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

    Description

    Agreement of VA functional status data with the reference standard of research-collected data for assessing dependence in activities of daily living, among patients with 2–4 weeks elapsed between assessments.a

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston (2023). Breakdown of demographics from the SET dataset by faculty. [Dataset]. http://doi.org/10.1371/journal.pone.0209749.t001

Breakdown of demographics from the SET dataset by faculty.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jun 21, 2023
Dataset provided by
PLOS ONE
Authors
Y. Fan; L. J. Shepherd; E. Slavich; D. Waters; M. Stone; R. Abel; E. L. Johnston
License

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

Description

Across the rows are: total number of individual student surveys; total number of unique courses; number of female teachers with non-English and English speaking background; number of male teachers with non-English (NE) and English (E) speaking background; and the number of female and male international (I) and local (L) students.

Search
Clear search
Close search
Google apps
Main menu