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50 Research products

  • Digital Humanities and Cultural Heritage
  • 2014-2023
  • Dataset
  • National Institutes of Health

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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: Guan, Meijian; Cho, Samuel; Petro, Robin; Zhang, Wei; +2 Authors

    Objectives: Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods: We obtained 5,889 de-identified progress reports (2,439 words on average) for 755 cancer patients who have undergone a clinical Next Generation Sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit (GRU), long-short term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to five machine learning algorithms including Naive Bayes (NB), K-nearest Neighbor (KNN), Support Vector Machine for classification (SVC), Random Forest (RF), and Logistic Regression (LR). Results: Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pre-trained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion: NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes. NLP_Report_Process_cleanPython code to process raw clinical report.NonDL_models_cleanPython file used to perform classification with non-RNN models.LSTM_keras_03012018_cleanPython code to perform RNN models.visualizeModels_cleanPython code to create visualizations.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    ZENODO
    Dataset . 2019
    License: CC 0
    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/
    DRYAD; NARCIS
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; NARCIS
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      DRYAD; NARCIS
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; NARCIS
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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: Kotlar, Alex; Trevino, Cristina; Zwick, Michael; Cutler, David; +1 Authors

    Description of online comparison settings. (XLSX 34 kb)

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    figshare
    Dataset . 2018
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2018
    License: CC BY
    Data sources: Datacite
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      figshare
      Dataset . 2018
      License: CC BY
      Data sources: Datacite
      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/
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      Dataset . 2018
      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/
    Authors: W. Duncan Wadsworth; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; +2 Authors

    The dataset used for the KEGG orthology groups. (ZIP 718 kb)

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    figshare
    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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      figshare
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
      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/
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      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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  • Authors: Campbell, Cameron D.; Lee, James Z.;

    The population records in the CMGPD-SC are transcribed from the Eight Banner population registers preserved in the Liaoning Provincial Archives, which are also available digitally worldwide through the Genealogical Society of Utah. Like the populations covered by the CMGPD-LN dataset, the CMGPD-SC population also belonged to the Eight Banners, a civil and military administrative system established by the Qing dynasty (1644-1911) to govern the Manchurian and Mongolian provinces in Greater North and Northeast China and the Qing garrison populations in China proper. Specifically, the populations covered by CMGPD-SC were bannermen who previously lived in Beijing and the various garrisons located in Rehe and present day Liaoning and Jilin provinces. These populations moved to Shuangcheng between 1815 and 1838 under a government-organized migration. The population lived in 120 villages in Shuangcheng county in present day Heilongjiang in northeast China. The CMGPD-SC follows individuals prospectively with time-varying characteristics available annually for metropolitan and rural bannermen, who comprise over four-fifths of the CMGPD-SC population, and triennially for 'floating' bannermen, who comprise almost all the remaining population. The timing of key economic, social, family, and demographic events and transitions can thus be ascertained. Contextual information as to community and household are also available at regular intervals. The CMGPD-SC also contains rare longitudinal information on property in the form of individual landholding, which distinguishes it from many other comparable historical data sources, including the CMGPD-LN. The China Multi-Generational Panel Dataset - Shuangcheng (CMGPD-SC) provides longitudinal individual, household, and community information on the demographic and socioeconomic characteristics of a resettled population living in Shuangcheng, a county in present-day Heilongjiang Province of Northeastern China, for the period from 1866 to 1913. The dataset includes some 1.3 million annual observations of over 100,000 unique individuals descended from families who were relocated to Shuangcheng in the early 19th century. These families were divided into 3 categories based on their place of origin: metropolitan bannermen, rural bannermen, and floating bannermen. The CMGPD-SC, like its Liaoning counterpart, the CMGPD-LN (ICPSR 27063), is a valuable data source for studying longitudinal as well as multi-generational social and demographic processes. The population categories had salient differences in social origins and land entitlements, and landholding data are available at a number of time periods, thus the CMGPD-SC is especially suitable to the study of stratification processes. Datasets: DS0: Study-Level Files DS1: Basic and Analytic File DS2: Basic File (Restricted) DS3: Kinship File DS4: Disability File DS5: Position File DS6: Landholding File DS7: Landholding File (Restricted) DS8: Occupational File DS9: Grain Price File Smallest Geographic Unit: historical village record abstracts

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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: Stewart, Fiona A.; Piel, Alexander K.; Luncz, Lydia; Osborne, Joanna; +4 Authors

    Most of our knowledge of wild chimpanzee behaviour stems from fewer than 10 long-term field sites. This bias limits studies to a potentially unrepresentative set of communities known to show great behavioural diversity on small geographic scales. Here, we introduce a new genetic approach to bridge the gap between behavioural material evidence in unhabituated chimpanzees and genetic advances in the field of primatology. The use of DNA analyses has revolutionised archaeological and primatological fields, whereby extraction of DNA from non-invasively collected samples allows researchers to reconstruct behaviour without ever directly observing individuals. We used commercially available forensic DNA kits to show that termite-fishing by wild chimpanzees (Pan troglodytes schweinfurthii) leaves behind detectable chimpanzee DNA evidence on tools. We then quantified the recovered DNA, compared the yield to that from faecal samples, and performed an initial assessment of mitochondrial and microsatellite markers to identify individuals. From 49 termite-fishing tools from the Issa Valley research site in western Tanzania, we recovered an average of 52 pg/µl chimpanzee DNA, compared to 376.2 pg/µl in faecal DNA extracts. Mitochondrial DNA haplotypes could be assigned to 41 of 49 tools (84%). Twenty-six tool DNA extracts yielded >25 pg/µl DNA and were selected for microsatellite analyses; genotypes were determined with confidence for 18 tools. These tools were used by a minimum of 11 individuals across the study period and termite mounds. These results demonstrate the utility of bio-molecular techniques and a primate archaeology approach in non-invasive monitoring and behavioural reconstruction of unhabituated primate populations. DNA recovery from chimpanzee toolsFile contains chimpanzee tool sample, genotyping, and DNA quantification dataDyradData.xlsx

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    ZENODO
    Dataset . 2018
    License: CC 0
    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/
    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
    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/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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    Authors: Martinez, Andres; Hua, Jason; Haque, Ezazul; Thorne, Peter; +1 Authors

    Individual polychlorinated biphenyls (PCBs) in surficial urban soil from East Chicago, Indiana, USA, were measured and reported here from thirty-three sites. Gas chromatography (GC) in tandem with Mass Spectrometry GC-MS/MS (Agilent 7000) in multiple reaction monitoring (MRM) mode was used to quantify all 209 congeners in 171 individual or coeluting congener peaks. The GC was equipped with a Supelco SBP-Octyl capillary column (30 m × 0.25 mm ID, 0.25 mm film thicknesses) with helium as carrier gas. Individual PCB congener in surficial sediment using GC-MS/MS:* Values = 0 indicates non-detected* No limit of quantification (LOQ) correction was performed

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    This is the data that was used for a paper titled 'Altered basal ganglia output during self-restraint.' Format and organization is written in the ReadMe.txt file. Files will be continued to be uploaded until 10/30/22.

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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: Kotlar, Alex; Trevino, Cristina; Zwick, Michael; Cutler, David; +1 Authors

    :Description of online comparison settings. (XLSX 596 kb)

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  • The population records in the CMGPD-SC are transcribed from the Eight Banner population registers preserved in the Liaoning Provincial Archives, which are also available digitally worldwide through the Genealogical Society of Utah. Like the populations covered by the CMGPD-LN dataset, the CMGPD-SC population also belonged to the Eight Banners, a civil and military administrative system established by the Qing dynasty (1644-1911) to govern the Manchurian and Mongolian provinces in Greater North and Northeast China and the Qing garrison populations in China proper. Specifically, the populations covered by CMGPD-SC were bannermen who previously lived in Beijing and the various garrisons located in Rehe and present day Liaoning and Jilin provinces. These populations moved to Shuangcheng between 1815 and 1838 under a government-organized migration. The population lived in 120 villages in Shuangcheng county in present day Heilongjiang in northeast China. The CMGPD-SC follows individuals prospectively with time-varying characteristics available annually for metropolitan and rural bannermen, who comprise over four-fifths of the CMGPD-SC population, and triennially for 'floating' bannermen, who comprise almost all the remaining population. The timing of key economic, social, family, and demographic events and transitions can thus be ascertained. Contextual information as to community and household are also available at regular intervals. The CMGPD-SC also contains rare longitudinal information on property in the form of individual landholding, which distinguishes it from many other comparable historical data sources, including the CMGPD-LN. The China Multi-Generational Panel Dataset - Shuangcheng (CMGPD-SC) provides longitudinal individual, household, and community information on the demographic and socioeconomic characteristics of a resettled population living in Shuangcheng, a county in present-day Heilongjiang Province of Northeastern China, for the period from 1866 to 1913. The dataset includes some 1.3 million annual observations of over 100,000 unique individuals descended from families who were relocated to Shuangcheng in the early 19th century. These families were divided into 3 categories based on their place of origin: metropolitan bannermen, rural bannermen, and floating bannermen. The CMGPD-SC, like its Liaoning counterpart, the CMGPD-LN (ICPSR 27063), is a valuable data source for studying longitudinal as well as multi-generational social and demographic processes. The population categories had salient differences in social origins and land entitlements, and landholding data are available at a number of time periods, thus the CMGPD-SC is especially suitable to the study of stratification processes. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Datasets: DS0: Study-Level Files DS1: Basic and Analytic File DS2: Basic File (Restricted) DS3: Kinship File DS4: Disability File DS5: Position File DS6: Landholding File DS7: Landholding File (Restricted) DS8: Occupational File DS9: Grain Price File China Multi-Generational Panel Datasets Series Smallest Geographic Unit: historical village record abstracts

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    Authors: Kotlar, Alex; Trevino, Cristina; Zwick, Michael; Cutler, David; +1 Authors

    :Bystro vs VEP annotation comparison details. (XLSX 684 kb)

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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: Guan, Meijian; Cho, Samuel; Petro, Robin; Zhang, Wei; +2 Authors

    Objectives: Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods: We obtained 5,889 de-identified progress reports (2,439 words on average) for 755 cancer patients who have undergone a clinical Next Generation Sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit (GRU), long-short term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to five machine learning algorithms including Naive Bayes (NB), K-nearest Neighbor (KNN), Support Vector Machine for classification (SVC), Random Forest (RF), and Logistic Regression (LR). Results: Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pre-trained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion: NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes. NLP_Report_Process_cleanPython code to process raw clinical report.NonDL_models_cleanPython file used to perform classification with non-RNN models.LSTM_keras_03012018_cleanPython code to perform RNN models.visualizeModels_cleanPython code to create visualizations.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    DRYAD; NARCIS
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; NARCIS
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      DRYAD; NARCIS
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; NARCIS
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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: Kotlar, Alex; Trevino, Cristina; Zwick, Michael; Cutler, David; +1 Authors

    Description of online comparison settings. (XLSX 34 kb)

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    Dataset . 2018
    License: CC BY
    Data sources: Datacite
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    Dataset . 2018
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    Data sources: Datacite
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      Dataset . 2018
      License: CC BY
      Data sources: Datacite
      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/
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      Dataset . 2018
      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/
    Authors: W. Duncan Wadsworth; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; +2 Authors

    The dataset used for the KEGG orthology groups. (ZIP 718 kb)

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    Dataset . 2017
    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/ figsharearrow_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/
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      Dataset . 2017
      License: CC BY
      Data sources: Datacite
      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/
      figshare
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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