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  • Authors: Adkins, Josh; Law, Terry;
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  • Authors: A Boyle;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B Noble;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://doi.org/10.1...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.17989/encsr...
    Dataset . 2020
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://doi.org/10.1...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.17989/encsr...
      Dataset . 2020
      License: CC BY
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  • Authors: A Boyle;
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  • Authors: Moschini, Emily G.;

    Supplementary data for online appendix of "Child Care Subsidies and Child Skill Accumulation in One- and Two-Parent Families"

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  • Authors: Isayev, Olexandr;

    The dataset files contain low-energy conformers and tautomers of 6,433 FDA approved & investigational drugs. A collection consists of 32,036 tautomeric structures and ~3M conformers. After extensive sampling, for every tautomer we found low laying conformers within approx. 6 kcal/mol window using ANI-2x neural-network molecular potential. The ANI-2x potential is approaching the accuracy of high-level QM calculations (wB97x-D). [DOI: 10.26434/chemrxiv.11819268.v1 ] For each conformer we list total energy, relative energy, and dipole-consistent partial atomic charges. (~2.1GB zip file comprised of SDF files)

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  • Authors: Kamp Dush, Claire M.; Manning, Wendy D.; Van Riper, David;

    The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include state-level domestic violence and gun ownership, which denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable. DS1 "MATCH ID and State FIPS Data" provides a MATCH_ID variable and STATEFIPS variable to allow users to merge the IPUMS Contextual Determinants of Health (CDOH) data with the NCHAT (National Couples' Health and Time Study). DS2 "Sexism State Domestic Violence and Gun Ownership Data" denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. Please refer to page 2 of the User Guide for the formula used to create this binary variable. Once the NCHAT data is merged with the IPUMS CDOH data using DS1, it can then be merge with DS2 via the STATEFIPS variable. record abstractsDS1 is the exact same file as the other "MATCH ID and State FIPS Data" files from the other studies within this IPUMS Contextual Determinants of Health (CDOH) series.For DS2, the data is limited to the 50 United States. There is data for all 30 years from all 50 states and Washington from 1991 to 2020. There is no data for Washington D.C. or other U.S. territories. The Contextual Determinants of Health (CDOH) measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. DS2 includes 3 variables and 1,500 cases. There are variables for STATEFIPS and YEAR. The last variable, INDICATOR_DV is a binary variable (0 / 1) indicating whether a state has a law prohibiting domestic violence offenders from owning firearms in a particular year. The purpose of this study is to provide researchers the ability to merge the NCHAT (National Couples' Health and Time Study) data with the Contextual Determinants of Health (CDOH). Historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. Smallest Geographic Unit: State Datasets: DS0: Study-Level Files DS1: MATCH ID and State FIPS Data DS2: Sexism State Domestic Violence and Gun Ownership Data

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ashkan Kazemi; Kiran Garimella; Devin Gaffney; Scott A. Hale;

    Two datasets are included in this repository: claim matching and claim detection datasets. The collections contain data in 5 languages: Bengali, English, Hindi, Malayalam and Tamil. The "claim detection" dataset contains textual claims from social media and fact-checking websites annotated for the "fact-check worthiness" of the claims in each message. Data points have one of the three labels of "Yes" (text contains one or more check-worthy claims), "No" and "Probably". The "claim matching" dataset is a curated collection of pairs of textual claims from social media and fact-checking websites for the purpose of automatic and multilingual claim matching. Pairs of data have one of the four labels of "Very Similar", "Somewhat Similar", "Somewhat Dissimilar" and "Very Dissimilar". All personally identifiable information (PII) including phone numbers, email addresses, license plate numbers and addresses have been replaced with general tags (e.g. <PHONE#>, <ADDRESS>, etc) to protect user anonymity. A detailed explanation on the curation and annotation process is provided in our ACL 2021 paper: Kazemi, A.; Garimella, K.; Gaffney, D.; and Hale, S. A. 2021. Claim Matching Beyond English to Scale Global Fact-Checking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, ACL 2021.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • Authors: M Kellis;
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1,177,568 Research products
  • Authors: Adkins, Josh; Law, Terry;
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  • Authors: A Boyle;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B Noble;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://doi.org/10.1...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.17989/encsr...
    Dataset . 2020
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://doi.org/10.1...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.17989/encsr...
      Dataset . 2020
      License: CC BY
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  • Authors: A Boyle;
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  • Authors: Moschini, Emily G.;

    Supplementary data for online appendix of "Child Care Subsidies and Child Skill Accumulation in One- and Two-Parent Families"

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  • Authors: Isayev, Olexandr;

    The dataset files contain low-energy conformers and tautomers of 6,433 FDA approved & investigational drugs. A collection consists of 32,036 tautomeric structures and ~3M conformers. After extensive sampling, for every tautomer we found low laying conformers within approx. 6 kcal/mol window using ANI-2x neural-network molecular potential. The ANI-2x potential is approaching the accuracy of high-level QM calculations (wB97x-D). [DOI: 10.26434/chemrxiv.11819268.v1 ] For each conformer we list total energy, relative energy, and dipole-consistent partial atomic charges. (~2.1GB zip file comprised of SDF files)

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  • Authors: Kamp Dush, Claire M.; Manning, Wendy D.; Van Riper, David;

    The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include state-level domestic violence and gun ownership, which denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable. DS1 "MATCH ID and State FIPS Data" provides a MATCH_ID variable and STATEFIPS variable to allow users to merge the IPUMS Contextual Determinants of Health (CDOH) data with the NCHAT (National Couples' Health and Time Study). DS2 "Sexism State Domestic Violence and Gun Ownership Data" denotes whether a state has a law that prohibits domestic violence offenders from owning firearms above and beyond federal law. Please refer to page 2 of the User Guide for the formula used to create this binary variable. Once the NCHAT data is merged with the IPUMS CDOH data using DS1, it can then be merge with DS2 via the STATEFIPS variable. record abstractsDS1 is the exact same file as the other "MATCH ID and State FIPS Data" files from the other studies within this IPUMS Contextual Determinants of Health (CDOH) series.For DS2, the data is limited to the 50 United States. There is data for all 30 years from all 50 states and Washington from 1991 to 2020. There is no data for Washington D.C. or other U.S. territories. The Contextual Determinants of Health (CDOH) measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. DS2 includes 3 variables and 1,500 cases. There are variables for STATEFIPS and YEAR. The last variable, INDICATOR_DV is a binary variable (0 / 1) indicating whether a state has a law prohibiting domestic violence offenders from owning firearms in a particular year. The purpose of this study is to provide researchers the ability to merge the NCHAT (National Couples' Health and Time Study) data with the Contextual Determinants of Health (CDOH). Historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. Smallest Geographic Unit: State Datasets: DS0: Study-Level Files DS1: MATCH ID and State FIPS Data DS2: Sexism State Domestic Violence and Gun Ownership Data

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