CWTs: A Public Large-Scale Chinese Web Table Data Set

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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Financial distress determinants among SMEs: empirical evidence from Sweden

Journal of Economic Studies ◽

10.1108/jes-01-2019-0030 ◽

2020 ◽

Vol 47 (3) ◽

pp. 547-560 ◽

Cited By ~ 1

Author(s):

Darush Yazdanfar ◽

Peter Öhman

Keyword(s):

Financial Crisis ◽

Financial Distress ◽

Large Scale ◽

Global Financial Crisis ◽

Binary Logistic Regression ◽

Data Availability ◽

Cross Sectional ◽

Data Set ◽

Content Type ◽

The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

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Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

Earth Planets and Space ◽

10.1186/s40623-020-01286-z ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Chao Xiong ◽

Claudia Stolle ◽

Patrick Alken ◽

Jan Rauberg

Keyword(s):

Time Distribution ◽

Large Scale ◽

Particle Flux ◽

Particle Energy ◽

Lower Latitude ◽

Particle Precipitation ◽

Data Set ◽

The Mean ◽

Two Parameters ◽

Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2020.46 ◽

2020 ◽

Vol 37 ◽

Author(s):

Lior Shamir

Keyword(s):

Large Scale ◽

Spiral Galaxies ◽

Hubble Space Telescope ◽

Gravitational Interaction ◽

Large Data ◽

Sloan Digital Sky Survey ◽

Data Sets ◽

Dipole Axis ◽

Data Set ◽

The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2021.3051189 ◽

2021 ◽

pp. 1-13

Author(s):

Usman Naseem ◽

Imran Razzak ◽

Matloob Khushi ◽

Peter W. Eklund ◽

Jinman Kim

Keyword(s):

Sentiment Analysis ◽

Large Scale ◽

Data Set ◽

Twitter Data

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Distributed learning with indefinite kernels

Analysis and Applications ◽

10.1142/s021953051850032x ◽

2019 ◽

Vol 17 (06) ◽

pp. 947-975 ◽

Cited By ~ 2

Author(s):

Lei Shi

Keyword(s):

Large Scale ◽

Substantial Reduction ◽

Computation Time ◽

Distributed Learning ◽

Rates Of Convergence ◽

Regression Problem ◽

Data Set ◽

Regularization Scheme ◽

Original Algorithm ◽

Indefinite Kernel

We investigate the distributed learning with coefficient-based regularization scheme under the framework of kernel regression methods. Compared with the classical kernel ridge regression (KRR), the algorithm under consideration does not require the kernel function to be positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods. The distributed learning approach partitions a massive data set into several disjoint data subsets, and then produces a global estimator by taking an average of the local estimator on each data subset. Easy exercisable partitions and performing algorithm on each subset in parallel lead to a substantial reduction in computation time versus the standard approach of performing the original algorithm on the entire samples. We establish the first mini-max optimal rates of convergence for distributed coefficient-based regularization scheme with indefinite kernels. We thus demonstrate that compared with distributed KRR, the concerned algorithm is more flexible and effective in regression problem for large-scale data sets.

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Mobility patterns are associated with experienced income segregation in large US cities

Nature Communications ◽

10.1038/s41467-021-24899-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Esteban Moro ◽

Dan Calacci ◽

Xiaowen Dong ◽

Alex Pentland

Keyword(s):

Large Scale ◽

Mobility Model ◽

Coarse Grained ◽

Spatial Proximity ◽

Mobility Patterns ◽

Data Set ◽

Residential Patterns ◽

Income Segregation ◽

Mobility Data ◽

Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

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Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2018-214354 ◽

2019 ◽

Vol 78 (5) ◽

pp. 617-628 ◽

Cited By ~ 5

Author(s):

Erika Van Nieuwenhove ◽

Vasiliki Lagou ◽

Lien Van Eyck ◽

James Dooley ◽

Ulrich Bodenhofer ◽

...

Keyword(s):

Machine Learning ◽

Juvenile Idiopathic Arthritis ◽

Large Scale ◽

Inflammatory Diseases ◽

Adaptive Immune System ◽

Healthy Children ◽

Learning Approaches ◽

Data Set ◽

Immune Signature ◽

Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

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Demographic Characterization of Anonymous Trace Travel Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2526-03 ◽

2015 ◽

Vol 2526 (1) ◽

pp. 19-28 ◽

Cited By ~ 2

Author(s):

Joshua Auld ◽

Abolfazl (Kouros) Mohammadian ◽

Marcelo Simas Oliveira ◽

Jean Wolf ◽

William Bachman

Keyword(s):

Test Data ◽

Large Scale ◽

Travel Demand ◽

Null Model ◽

Demographic Characteristics ◽

Travel Pattern ◽

Data Set ◽

Trace Data ◽

Demographic Models ◽

Pattern Information

Research was undertaken to determine whether demographic characteristics of individual travelers could be derived from travel pattern information when no information about the individual was available. This question is relevant in the context of anonymously collected travel information, such as cell phone traces, when used for travel demand modeling. Determining the demographics of a traveler from such data could partially obviate the need for large-scale collection of travel survey data, depending on the purpose for which the data were to be used. This research complements methodologies used to identify activity stops, purposes, and mode types from raw trace data and presumes that such methods exist and are available. The paper documents the development of procedures for taking raw activity streams estimated from GPS trace data and converting these into activity travel pattern characteristics that are then combined with basic land use information and used to estimate various models of demographic characteristics. The work status, education level, age, and license possession of individuals and the presence of children in their households were all estimated successfully with substantial increases in performance versus null model expectations for both training and test data sets. The gender, household size, and number of vehicles proved more difficult to estimate, and performance was lower on the test data set; these aspects indicate overfitting in these models. Overall, the demographic models appear to have potential for characterizing anonymous data streams, which could extend the usability and applicability of such data sources to the travel demand context.

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