Supplementary Datasets Research Articles

Nitrogen and iron availability drive metabolic remodeling and natural selection of diverse phytoplankton during experimental upwelling: Supplementary Figures and Datasets

Nitrogen and iron availability drive metabolic remodeling and natural selection of diverse phytoplankton during experimental upwelling: Supplementary Figures and Datasets

Supplementary data sets

Supplementary data sets

Supplementary data sets

Supplementary data sets

Harmonized chronologies of a global late Quaternary pollen dataset (LegacyAge 1.0)

Abstract. We present a chronology framework named LegacyAge 1.0 containing harmonized chronologies for 2831 pollen records (downloaded from the Neotoma Paleoecology Database and the supplementary Asian datasets) together with their age control points and metadata in machine-readable data formats. All chronologies use the Bayesian framework implemented in Bacon version 2.5.3. Optimal parameter settings of priors (accumulation.shape, memory.strength, memory.mean, accumulation.rate, and thickness) were identified based on information in the original publication or iteratively after preliminary model inspection. The most common control points for the chronologies are radiocarbon dates (86.1 %), calibrated by the latest calibration curves (IntCal20 and SHCal20 for the terrestrial radiocarbon dates in the Northern Hemisphere and Southern Hemisphere and Marine20 for marine materials). The original publications were consulted when dealing with outliers and inconsistencies. Several major challenges when setting up the chronologies included the waterline issue (18.8 % of records), reservoir effect (4.9 %), and sediment deposition discontinuity (4.4 %). Finally, we numerically compare the LegacyAge 1.0 chronologies to those published in the original publications and show that the reliability of the chronologies of 95.4 % of records could be improved according to our assessment. Our chronology framework and revised chronologies provide the opportunity to make use of the ages and age uncertainties in synthesis studies of, for example, pollen-based vegetation and climate change. The LegacyAge 1.0 dataset, including metadata, datings, harmonized chronologies, and R code used, is open-access and available at PANGAEA (https://doi.org/10.1594/PANGAEA.933132; Li et al., 2021) and Zenodo (https://doi.org/10.5281/zenodo.5815192; Li et al., 2022), respectively.

A regret theory-based three-way decision approach with three strategies

Breast cancer is a malignant tumor that seriously threatens women’s health. Although classic multi-attribute decision-making (MADM) techniques can handle this kind of medical problem, a decision-maker (DM) can only collect sample data under various indicators for result ranking and analysis. The three-way decision (3WD) theory further supplements a classification scheme. In addition, a DM’s own limited rationality and personality traits have a strong impact on decision-making results. By using a dominance-based rough set approach (DRSA), a new 3WD method based on the regret theory (RT) with optimistic, neutral and pessimistic strategies (3WD-RT-OEP) is constructed in a fuzzy environment, so as to prevent diseases in advance and improve the survival rate of patients. First, attribute weights are calculated based on the similarity between the defined attributes of dominance classes, and a new method for calculating conditional probabilities is proposed to enhance the objectivity of the method by the similarity between object dominance classes and decision classes. Second, the score functions of three strategies are proposed by considering regret and rejoicing values in RT, and specific steps and algorithms of the 3WD-RT-OEP method are given as well. Finally, the validity and rationality of the constructed method are proved by experimental analysis with the support of case studies and supplementary data sets.

Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

In this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (LjSCVW) is measured via new supplementary experimental datasets via most popular vertex angles θ (128° and 60°). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of LjSCVW versus the θ, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (Nst) in the SCVW, and various tailgate angles (ɸ). Via dimensional analysis and pre-processing method, most effectual parameters on the LjSCVW were obtained as Fr1 (approaching Froude number), Re1 (incoming Reynolds number), and (relative sequent depths). Based on the experimental results, by increasing the values of Fr1 and Re1, the values of LjSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the LjSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the LjSCVW. According to the attained statistical indices (determination coefficient (R2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the LjSCVW with high accuracy.

The Expanded Use of DOI and Content Citation Granularity

CSE 2020 Virtual Annual Meeting MODERATOR: Nancy R Gough Publishing Consultant BioSeredipity, LLC Elkridge, Maryland SPEAKERS: Midori Baer Director Publishing Operations PLOS Washington, DC Stacy Konkiel Director Research Relations Almetrics & Dimensions, Digital Science Minneapolis, Minnesota Daniella Lowenberg Dryad Product Manager & Make Data Count Lead California Digital Library San Francisco, California REPORTER: Beverly Lindeen Senior Managing Editor Allen Press, Inc. Lawrence, Kansas It is pretty well known that DOIs (digital object identifiers) are used for articles that are published in an online format. However, they are also necessary for the various parts of an article, such as preprints, figures, tables, databases, data, methods, etc. Nancy Gough moderated this session looking at how DOIs for these items is used by publishers and indexing services. In her presentation entitled “Expanded Use of DOIs at PLOS,” Midori Baer, Director Publishing Operations at PLOS, reviewed the ways in which PLOS uses DOIs as part of their larger organizational mission of “empowering researchers to accelerate progress in science and medicine by leading a transformation in research communication.” The continuum of research is important, as is the abilityof the authors to tell the stories of their science. Research needs to be discoverable. Article assets (supplementary information and datasets, tables, figures, peer-review reports, etc.) are assigned DOIs and are registered as component DOIs with Crossref. A link to the DOI appears in both the html and PDF views of all the article assets. There are currently limitations. On the PLOS website, it would be beneficial to […]

Acting with Inherently Uncertain Data: Practices of Data-Centric Knowing

Data-driven data science challenges our conceptualization of “data.” Significantly beyond capturing a given phenomenon, data, increasingly, are the phenomenon. Data may be iteratively manipulated algorithmically, undermining the “faithfulness” of data to any originating phenomenon. Crucially, data that are not “faithful” are inherently uncertain, as data risk becoming meaningless symbols. We empirically study how a community of commercially based geoscientists grapples with the phenomenon of offshore oil and gas reservoirs located kilometers below the seabed. The data available about these reservoirs are algorithmically manipulated sensor-based Internet of Things data. Our main contribution is the articulation of three patterns of work practices detailing how inherently uncertain data are woven into consequential work practices: (1) Accumulating is the cumulative process of supporting and triangulating one set of data with supplementary data sets; accumulating captures the conservative approach of backing up existing interpretations of the data. (2) Reframing describes the process where existing interpretations are contested by new data or models; reframing captures that there are limits to how far data can be manipulated. (3) Prospecting, or the cultivation of competing, incompatible data interpretations; while the former two patterns essentially attempt to regulate uncertainty, prospecting is about embracing it. Our concept of data-centric knowing is constituted by these three interwoven, ongoing practices.

Intelligent prescription-generating models of traditional chinese medicine based on deep learning

Objective: This study aimed to construct an intelligent prescription-generating (IPG) model based on deep-learning natural language processing (NLP) technology for multiple prescriptions in Chinese medicine. Materials and Methods: We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation, and the Yellow Emperor's Canon of Internal Medicine, the Classic of the Miraculous Pivot, and the Classic on Medical Problems as supplementary datasets for fine-tuning. We selected the word-embedding model based on the Imperial Collection of Four, the bidirectional encoder representations from transformers (BERT) model based on the Chinese Wikipedia, and the robustly optimized BERT approach (RoBERTa) model based on the Chinese Wikipedia and a general database. In addition, the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model. Multiple IPG models were constructed based on the pretraining strategy and experiments were performed. Metrics of precision, recall, and F1-score were used to assess the model performance. Based on the trained models, we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns. Results: Among all the trained models, the RoBERTa-large model performed the best, with a test set precision of 92.22%, recall of 86.71%, and F1-score of 89.38% and 10-fold cross-validation precision of 94.5% ± 2.5%, recall of 90.47% ± 4.1%, and F1-score of 92.38% ± 2.8%. The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer's patterns showed the associations of symptom–symptoms, disease–symptoms, and symptom–punctuations, while the between-layer's patterns showed a progressive or dynamic symptom and disease transformation. Conclusions: Deep-learning-based NLP technology significantly improves the performance of IPG model. In addition, NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.

A High-Resolution Map of Singapore’s Terrestrial Ecosystems

The natural and semi-natural areas within cities provide important refuges for biodiversity, as well as many benefits to people. To study urban ecology and quantify the benefits of urban ecosystems, we need to understand the spatial extent and configuration of different types of vegetated cover within a city. It is challenging to map urban ecosystems because they are typically small and highly fragmented; thus requiring high resolution satellite images. This article describes a new high-resolution map of land cover for the tropical city-state of Singapore. We used images from WorldView and QuickBird satellites, and classified these images using random forest machine learning and supplementary datasets into 12 terrestrial land classes. Close to 50 % of Singapore’s land cover is vegetated while freshwater fills about 6 %, and the rest is bare or built up. The overall accuracy of the map was 79 % and the class-specific errors are described in detail. Tropical regions such as Singapore have a lot of cloud cover year-round, complicating the process of mapping using satellite imagery. The land cover map provided here will have applications for urban biodiversity studies, ecosystem service quantification, and natural capital assessment.

Is the maximum adsorption capacity obtained at high VOC pressures (>1000 Pa) really meaningful in real-world applications for the sorptive removal of VOCs under ambient conditions (

The general performance metric for sorbent is its maximum adsorption capacity in which the maximum amount of an adsorbate loadable on an adsorbent is determined from isotherms. Such a metric is useful to ascertain the maximum possible capacity obtained at high adsorbate partial pressures (e.g., >1,000 Pa). However, this is often misleading to predict the performance of materials in real-world applications where the ambient trace pollutant levels are much lower, e.g., <0.1 Pa. The use of such concept at such low pressure condition is also very problematic if the isotherm follows Henry's law. Overall, although such a metric is commonly reported, its utility is significantly limited for treating trace level pollutants. This study demonstrates the inadequacy of such definition based on experimental and theoretical basis. It is thus recommended that researchers must provide a complete isotherm analysis (e.g., Langmuir or Henry’s law) in the 0–10 Pa pressure region as supplementary datasets in support of any bold claims that a proposed sorbent is very fit for low pollutant pressure (<10 Pa) air quality management (AQM) purposes. Also, if the adsorption isotherm volumetric method is used, it is suggested that the isotherms should be constructed to cover a good range of intervals over 0–10 Pa region for AQM purposes (e.g., 0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, and 10 Pa). It is futher recommended, if possible, to expand such test over an extended range (e.g., 30, 100, 300, and 1000 Pa) for a facile assessment of performance on a log-log isotherm plot.

The Niakhar Social Networks and Health Project

This paper presents details of the design and implementation of the Niakhar Social Networks and Health Project (NSNHP), a large, mixed-methods project funded by the U.S. National Institute of General Medical Sciences (NIGMS). By redressing fundamental problems in conventional survey network data collection methods, the project is aimed at improving inferences concerning the association between social network structures and processes and health behaviors and outcomes. Fielded in collaboration with an ongoing demographic and health surveillance system in rural Senegal, the NSNHP includes qualitative data concerning the dimensions of social association and health ideologies and behaviors in the study zone, two panels of a new social network survey, and several supplementary and affiliated data sets.•Longitudinal social network survey linked to pre-existing surveillance data•Addresses fundamental methodological constraints in previous social network data•Enables social network analyses of health beliefs, behaviors, and outcomes

Observing the Tropical Atmosphere in Moisture Space

Measurements from the Barbados Cloud Observatory are analyzed to identify the processes influencing the distribution of moist static energy and the large-scale organization of tropical convection. Five years of water vapor and cloud profiles from a Raman lidar and cloud radar are composed to construct the structure of the observed atmosphere in moisture space. The large-scale structure of the atmosphere is similar to that now familiar from idealized studies of convective self-aggregation, with shallow clouds prevailing over a moist marine layer in regions of low-rank humidity, and deep convection in a nearly saturated atmosphere in regions of high-rank humidity. With supplementary reanalysis datasets the overall circulation pattern is reconstructed in moisture space, and shows evidence of a substantial lower-tropospheric component to the circulation. This shallow component of the circulation helps support the differentiation between the moist and dry columns, similar to what is found in simulations of convective self-aggregation. Radiative calculations show that clear-sky radiative differences can explain a substantial part of this circulation, with further contributions expected from cloud radiative effects. The shallow component appears to be important for maintaining the low gross moist stability of the convecting column. A positive feedback between a shallow circulation driven by differential radiative cooling and the low-level moisture gradients that help support it is hypothesized to play an important role in conditioning the atmosphere for deep convection. The analysis suggests that the radiatively driven shallow circulations identified by modeling studies as contributing to the self-aggregation of convection in radiative–convective equilibrium similarly play a role in shaping the intertropical convergence zone and, hence, the large-scale structure of the tropical atmosphere.

Seasonal and Interannual Mixed‐Layer Heat Budget Variability in the Western Tropical Atlantic From Argo Floats (2007–2012)

AbstractOceanic and atmospheric processes were investigated in order to explore the causes of seasonal and interannual variability of sea surface temperatures (SST) in the western tropical Atlantic (WTA; 20°S–20°N, 15°W–60°W). A mixed‐layer (ML) heat budget was performed by using Argo profiles and supplementary data sets based on satellite and atmospheric products during the period 2007–2012. The WTA is divided into four boxes which represent the main temporal and spatial heterogeneities of this region. An analysis of error of each term pointed out that the mean net surface heat fluxes are systematically underestimated by 20 W m−2. A correction of this term provides realistic estimates of the vertical mixing which was obtained as residual term. In agreement with previous studies, the results show that surface flux is the most important process that governs the seasonal cycle of the heat content. Changes in shortwave radiation and latent heat fluxes dictate the oceanic response to the meridional migration of the ITCZ. Along the equator, surface fluxes modulate the annual cycle of ML temperature, but are strongly balanced by horizontal advection. The entrainment term proves a small contribution to the cooling of the ML. On an interannual time scale, the strong positive (negative) SST anomalies observed in 2010 (2012) were generated during the previous winter in both years, mainly north of 10°N, during which the wind anomalies were at the origin of intense heat loss anomalies. Horizontal advection may contributes to the maintaining of these SST anomalies in the equatorial zone and south Atlantic.

Identification of forest cutting in managed forest of Haldwani, India using ALOS-2/PALSAR-2 SAR data

Large-scale forest clear-cut identification is one of the major application of remote sensing techniques. ALOS-2/PALSAR-2 is the latest SAR satellite providing multi-polarized L-band SAR data. With increasing deforestation, it is important to assess the potential of SAR data for identifying clear-cuts in forest regions. In this research work, multi-temporal ALOS-2/PALSAR-2 SAR data and supplementary Landsat-8 optical data sets are acquired over Indian tropical forest, and SAR parameters are analysed over a progressively clear-cut Teak plantation. Sensitivity of the SAR parameters to progressive clear-cuts is estimated and found that the cross-polarized backscatter σHV0 and entropy parameter H are most sensitive to both partial and complete clear-cut in forest compartments. An entropy thresholding based classification is carried out to identify clear-cut regions with a good accuracy. The study highlights the utility of SAR parameters to monitor forest clear-cuts for better forest management.

An Enhanced PIRATA Dataset for Tropical Atlantic Ocean–Atmosphere Research

AbstractThe Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) provides measurements of the upper ocean and near-surface atmosphere at 18 locations. Time series from many moorings are nearly 20 years in length. However, instrumental biases, data dropouts, and the coarse vertical resolutions of the oceanic measurements complicate their use for research. Here an enhanced PIRATA dataset (ePIRATA) is presented for the 17 PIRATA moorings with record lengths of at least seven years. Data in ePIRATA are corrected for instrumental biases, temporal gaps are filled using supplementary datasets, and the subsurface temperature and salinity time series are mapped to a uniform 5-m vertical grid. All original PIRATA data that pass quality control and that do not require bias correction are retained without modification, and detailed error estimates are provided. The terms in the mixed-layer heat and temperature budgets are calculated and included, with error bars. As an example of ePIRATA’s application, the vertical exchange of heat at the base of the mixed layer (Q−h) is calculated at each PIRATA location as the difference between the heat storage rate and the sum of the net surface heat flux and horizontal advection. Off-equatorial locations are found to have annual mean cooling rates of 20–60 W m−2, while cooling at equatorial locations reaches 85–110 W m−2 between 10° and 35°W and decreases to 40 W m−2 at 0°. At most off-equatorial locations, the strongest seasonal cooling from Q−h occurs when winds are weak. Possible explanations are discussed, including the importance of seasonal modulations of mixed-layer depth and the diurnal cycle.

Unravelling &amp; Strategic Disclosure: Evidence from the Hospitality Industry

We consider strategic disclosure and test the unravelling principle in a new market by analysing TripAdvisor admissions made by hoteliers regionally and internationally. We construct a unique dataset and verify disclosures for 4,357 hotels across 22 locations. The major finding is that a downward linear relationship exists between ratings and disclosure. The strict equilibrium prediction -full information revelation- fails. Lower ranked hotels tend to veil TripAdvisor ratings. Evaluating interactions between ratings and internal certification indicates that the latter may serve as an anchor for disclosure. We compile two supplementary datasets from holiday catalogues and Trustpilot to answer ancillary questions arising from the findings. Our research has implications for guest experiences and business ethics as observing information gaps is psychologically challenging.

Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models

Complex natural phenomena are increasingly investigated by the use of a complex computer simulator. To leverage the advantages of simulators, observational data need to be incorporated in a probabilistic framework so that uncertainties can be quantified. A popular framework for such experiments is the statistical computer model calibration experiment. A limitation often encountered in current statistical approaches for such experiments is the difficulty in modeling high-dimensional observational datasets and simulator outputs as well as high-dimensional inputs. As the complexity of simulators seems to only grow, this challenge will continue unabated. In this article, we develop a Bayesian statistical calibration approach that is ideally suited for such challenging calibration problems. Our approach leverages recent ideas from Bayesian additive regression Tree models to construct a random basis representation of the simulator outputs and observational data. The approach can flexibly handle high-dimensional datasets, high-dimensional simulator inputs, and calibration parameters while quantifying important sources of uncertainty in the resulting inference. We demonstrate our methodology on a CO2 emissions rate calibration problem, and on a complex simulator of subterranean radionuclide dispersion, which simulates the spatial–temporal diffusion of radionuclides released during nuclear bomb tests at the Nevada Test Site. Supplementary computer code and datasets are available online.

Erratum: Temporal dynamics of Puumala hantavirus infection in cyclic populations of bank voles

Scientific Reports 6: Article number: 21323; published online: 18 February 2016; updated: 13 April 2016 Supplementary Datasets 2–5 were inadvertently published with the original version of this Article. These have now been removed.

Accounting for China's Saving‐Investment Imbalance from 2002–2008

This paper decomposes and analyzes China's saving‐investment imbalance (equivalent to current account imbalance) from 2002–2008. We first use the Flow of Funds Accounts to calculate the saving and investment rates (propensity) of the household, corporate and government sectors and to evaluate their relative contribution to the aggregate saving‐investment surplus. The results indicate that the increase of saving‐investment surplus can be attributed to the steady increase of saving by the household and government sectors and the short‐term downsizing of investment by the corporate and government sectors. We then use more disaggregate supplementary datasets to explore the factors behind the evolution of the saving and investment rates for the three sectors. The rise of the household saving rate mainly sources from the urban sector. The corporate saving rate experienced a steady increase because of the rise of profitability. Government macroeconomic policies have had a strong influence on the saving and investment patterns of the corporate and government sectors.