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  • Digital Humanities and Cultural Heritage
  • 2014-2023
  • Dataset
  • US

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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: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    XRF Analysis: Archive halves of sections from Core MD01-2444 were analysed using an Avaatech XRF core scanner at the University of Cambridge. The core surface was carefully scraped cleaned and covered with a 4-mm thin SPEXCertiPrep Ultralene foil to avoid contamination and minimize desiccation. Each section was measured using a current of 0.2mA at three different voltages: 10 kV, 30 kV using a thin lead filter, and 50 kV using a copper filter. XRF data were collected every 2.5 mm. The length and width of the irradiated surface was 2.5 and 12 mm, respectively, with a count time of 40s. The record was generated by D.A. Hodell and S. Crowhurst. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

    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/ PANGAEAarrow_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/
    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
    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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      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/ PANGAEAarrow_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/
      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
      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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  • 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: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    Planktonic foraminifera (G. bulloides) oxygen isotopes: Measurements of foraminiferal stable isotopes were carried out in the Godwin Laboratory for Palaeoclimate Research (University of Cambridge), using a VG SIRA mass spectrometer. Analyses were made on samples consisting of 30 specimens of G. bulloides in the 300-355 mm size fraction, picked by M.J. Mleneck-Vautravers. Results are reported with reference to the international standard VPDB and the precision is better than ±0.08‰ for d18O. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

    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/ PANGAEA - Data Publi...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/
    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/
    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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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/ PANGAEA - Data Publi...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/
      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/
      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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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.

    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-EASYarrow_drop_down
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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
    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 . 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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      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-EASYarrow_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/
      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
      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 . 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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  • 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: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    Benthic foraminifera oxygen isotopes: The benthic isotope record was generated by the late N.J. Shackleton. Measurements were carried out in the Godwin Laboratory for Palaeoclimate Research (University of Cambridge), using a VG PRISM mass spectrometer. Where possible two or three separate analyses of different benthic species were made in each sample; a correction factor was applied according to the species and the average of all the corrected values at each level is shown in the figures. The following species were analysed, and adjusted as indicated: Cibicidoides sp.: +0.51; Uvigerina peregrina and similar specimens: 0.0; Globobulimina affinis: -0.4; Cibicidoides wuellerstorfi, +0.64; Globocassidulina sp.: -0.1; Hoeglundina elegans: -0.7. These adjustments are optimized for this particular core in accordance with the long-standing convention by which Uvigerina peregrina is assumed to deposit oxygen in isotopic equilibrium. Results are reported with reference to the international standard VPDB and the precision is better than ±0.08‰ for d18O. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

    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/ PANGAEA - Data Publi...arrow_drop_down
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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/
    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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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/ PANGAEA - Data Publi...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/
      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/
      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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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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      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
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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/
      figshare
      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: Mozzherin, Dmitry; Myltsev, Alexander; Patterson, David;

    1,000 name-strings randomly selected from GNI and used to determine Accuracy, Precision and Recall data (Table 1). (TXT 32 kb)

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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/
    figshare
    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
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      figshare
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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      Dataset . 2017
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    Authors: Young, Andrew; Lemmon, Alan; Skevington, Jeffrey; Ximo Mengual; +9 Authors

    Final Probe Regions. Complete listing of all regions used in alignments to generate probes. (XLSX 1446Â kb)

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    Dataset . 2016
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    Dataset . 2016
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      Dataset . 2016
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      Dataset . 2016
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    Authors: Tiee, Madeline S.; Harrigan, Ryan J.; Thomassen, Henri A.; Smith, Thomas B.;

    Data organized by species with 95% CI

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    Dataset . 2018
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    Dataset . 2018
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    Dataset . 2018
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      Dataset . 2018
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      Dataset . 2018
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      Dataset . 2018
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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: Rhodes, Rachael H; Brook, Edward J;

    Description and NotesDescription: Methane concentration from the Greenland NEEM-2011-S1 Ice Core from 71 to 408m depth (~270-1961 CE). Methane concentrations analysed online by laser spectrometer (SARA, Spectroscopy by Amplified Resonant Absorption, developed at Laboratoire Interdisciplinaire de Physique, Grenoble, France) on gas extracted from an ice core processed using a continuous melter system (Desert Research Institute). Methane data have a 5 second integration time (raw data acquisition rate 0.6 Hz). Analytical precision, from Allan Variance test, is 0.9 ppb (2 sigma). Long-term reproducibility is 2.6% (2 sigma). Gaps in the record are due to problems during online analysis. Online analysis conducted August-September 2011.Note: Lat-Long provided is for main NEEM borehole. The NEEM-2011-S1 core was drilled 200 m distance away in 2011 to 410 m depth.Methane concentrations are reported on NOAA2004 scale (instrument calibrated on dry synthetic air standards).A correction factor of 1.079 has been applied to all data to correct for methane dissolution in melted ice core sample prior to gas extraction. Correction factor calculated using empirical data (concentrations not aligned/tied to existing discrete methane measurements).Additional methods description provided in:* Stowasser, C., Buizert, C., Gkinis, V., Chappellaz, J., Schupbach, S., Bigler, M., Fain, X., Sperlich, P., Baumgartner, M., Schilt, A., Blunier, T., 2012. Continuous measurements of methane mixing ratios from ice cores. Atmos. Meas. Tech. 5, 999-1013.* Morville, J., Kassi, S., Chenevier, M., Romanini, D., 2005. Fast, low-noise, mode bymode, cavity-enhanced absorption spectroscopy by diode-laser self-locking. Appl. Phys. B Lasers Opt. 80, 1027-01038.* NEEM (North Greenland Eemian Ice Drilling) project information http://neem.dk/ NEEM-2011-S1 CH4 outliers.Data points removed from dataset according to specified cut-off value.Please refer to Rhodes et al. (2013) for full discussion of origins outlying data points. Briefly, these high frequency features are not artifacts of the continuous method and have been replicated by traditional discrete analyses. Comparison to chemistry measurements suggests they are related to biological in situ production of methane.

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    PANGAEA
    Dataset . 2019
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      PANGAEA
      Dataset . 2019
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    Authors: Oborny, Stephan C.; Cramer, Bradley D.; Brett, Carlton E.;

    Silurian strata within the tristate area of Indiana, Ohio, and Kentucky have been thoroughly studied for well over a century. Due to rapid facies changes throughout the region numerous lithostratigraphic terminologies were established, many of which were difficult to correlate even over short distances. Recently these stratigraphic complexities have received renewed interest due to advancements in bio-, chemo-, and sequence stratigraphy that greatly improved our understanding of the tristate area. These improvements had significant implications for our ability to correlate Rhuddanian through lower Sheinwoodian strata between the Appalachian and Illinois basins and allowed unified nomenclatural and sequence stratigraphic hierarchies for this interval to be developed across the Cincinnati Arch. Recent advancements in regional chronostratigraphy of Silurian strata, however, highlight several discrepancies with regards to the 3rd- and 4th-order sequence stratigraphic hierarchies assigned within these basins. In an effort to alleviate these points of disagreement, we contribute new δ13Ccarb chemostratigraphic and sequence stratigraphic analyses of a basinward Appalachian section from Scioto County, Ohio, spanning upper Telychian through Gorstian strata assigned to the Estill through Tymochtee formations, complemented by gamma-ray core scans for localities traversing northwestward from the sampled core section into western Ohio. These new data allow unified nomenclatural and sequence stratigraphic hierarchies to be developed throughout the region for strata spanning the upper Telychian through Gorstian stages. Additionally, these findings highlight a significant shift in the primary sites of sediment accommodation during the studied depositional interval.

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    Dataset . 2020
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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: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    XRF Analysis: Archive halves of sections from Core MD01-2444 were analysed using an Avaatech XRF core scanner at the University of Cambridge. The core surface was carefully scraped cleaned and covered with a 4-mm thin SPEXCertiPrep Ultralene foil to avoid contamination and minimize desiccation. Each section was measured using a current of 0.2mA at three different voltages: 10 kV, 30 kV using a thin lead filter, and 50 kV using a copper filter. XRF data were collected every 2.5 mm. The length and width of the irradiated surface was 2.5 and 12 mm, respectively, with a count time of 40s. The record was generated by D.A. Hodell and S. Crowhurst. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

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    PANGAEA
    Dataset . 2020
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      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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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: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    Planktonic foraminifera (G. bulloides) oxygen isotopes: Measurements of foraminiferal stable isotopes were carried out in the Godwin Laboratory for Palaeoclimate Research (University of Cambridge), using a VG SIRA mass spectrometer. Analyses were made on samples consisting of 30 specimens of G. bulloides in the 300-355 mm size fraction, picked by M.J. Mleneck-Vautravers. Results are reported with reference to the international standard VPDB and the precision is better than ±0.08‰ for d18O. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

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    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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    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
      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 . 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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    Authors: Tzedakis, Polychronis C; Margari, Vasiliki; Skinner, Luke C; Menviel, Laurie; +7 Authors

    doi: <