cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5788438
title: Co-occurrences of trending keywords in popular tech media (01.2016-04.2021)

description: Sources with weights Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues have been selected (e.g. 'metoo', 'gdpr') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.3243949
title: Co-occurrences of trending keywords in popular tech media (01.2016-04.2019)

description: Sources with weights Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues have been selected (e.g. 'metoo', 'gdpr') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.3243948
title: Co-occurrences of trending keywords in popular tech media (01.2016-04.2019)

description: Sources with weights Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues have been selected (e.g. 'metoo', 'gdpr') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.5464653
title: Data for "Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird"

description: <strong>Abstract</strong> To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, <em>Montifringilla nivalis</em>, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as potential causes for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.
DOI: 10.5281/zenodo.5464652
title: Data for "Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird"

description: <strong>Abstract</strong> To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, <em>Montifringilla nivalis</em>, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as potential causes for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.
DOI: 10.15129/77a8fbd3-15b6-424b-a88d-c920f2b812c0
title: A 2016 UK SAM with households disaggregated by income

description: This is a 2016 UK Social Accounting Matrix (SAM) where the households have been disaggregated into quintiles based on the households' gross income. This SAM incorporates the latest input-output tables published by the Office for National Statistics, while information from the Living Cost and Food survey have been used to disaggregate the households. This SAM is useful for projects looking to identify not only the economy-wide impacts of shocks to the UK economy, but also explore how households of different income are impacted. Moreover, it allows the exploration of shocks to specific household quintiles and how these could affect the wider economy.
DOI: 10.5281/zenodo.5770588
title: Data Set For: Raw Data Collected From NO2, O3 And NO Air Pollution Electrochemical Low-Cost Sensors

description: This data set contains data from two Captor nodes. These are prototypes nodes that were developed at the Universitat Politècnica de Catalunya (UPC) in order to study the effects of the sensor data gathering process on the use of low-cost sensors for air quality monitoring. Specifically, the captors nodes have tropospheric ozone, nitrogen dioxide, and nitrogen monoxide electrochemical sensors. They also have a temperature and relative humidity sensor inside the box. <br> Two Captor nodes were placed in a reference station in Barcelona (Spain) for four months (January 2021 to May 2021), at a sampling frequency of 0.5 Hz. The data set contains a "readme" file with a brief description of the five files that make up the data set.
description: This work was supported by the National Spanish project PID2019-107910RB-I00, and regional project 2017SGR-990.
DOI: 10.22008/fk2/nvro0e/h6xcry
title: G1000_Caledonian_Orogen_400dpi.tif

description: :unav
DOI: 10.22008/fk2/nvro0e/4x328u
title: G1000_Caledonian_Orogen_400dpi.pdf

description: :unav
DOI: 10.6084/m9.figshare.13368924
title: Topic Modeling on Triage Notes With Semiorthogonal Nonnegative Matrix Factorization

description: Emergency department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-bed for a patient is delayed until a doctor makes an admission decision. In this case study, we build a classifier to predict the disposition of patients using manually typed nurse notes collected during triage as provided by the Alberta Medical Center. These predictions can potentially be incorporated to early bed coordination and fast track streaming strategies to alleviate overcrowding and waiting times in the ED. However, these triage notes involve high dimensional, noisy, and sparse text data, which make model-fitting and interpretation difficult. To address this issue, we propose a novel semiorthogonal nonnegative matrix factorization for both continuous and binary predictors to reduce the dimensionality and derive word topics. The triage notes can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. Our real data analysis shows that the triage notes contain strong predictive information toward classifying the disposition of patients for certain medical complaints, such as altered consciousness or stroke. Additionally, we show that the document-topic vectors generated by our method can be used as features to further improve classification accuracy by up to 1% across different medical complaints, for example, 74.3%–75.3% accuracy for patients with stroke symptoms. This improvement could be clinically impactful for certain patients, especially when the scale of hospital patients is large. Furthermore, the generated word-topic vectors provide a bi-clustering interpretation under each topic due to the orthogonal formulation, which can be beneficial for hospitals in better understanding the symptoms and reasons behind patients’ visits. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13368924.v2
title: Topic Modeling on Triage Notes With Semiorthogonal Nonnegative Matrix Factorization

description: Emergency department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-bed for a patient is delayed until a doctor makes an admission decision. In this case study, we build a classifier to predict the disposition of patients using manually typed nurse notes collected during triage as provided by the Alberta Medical Center. These predictions can potentially be incorporated to early bed coordination and fast track streaming strategies to alleviate overcrowding and waiting times in the ED. However, these triage notes involve high dimensional, noisy, and sparse text data, which make model-fitting and interpretation difficult. To address this issue, we propose a novel semiorthogonal nonnegative matrix factorization for both continuous and binary predictors to reduce the dimensionality and derive word topics. The triage notes can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. Our real data analysis shows that the triage notes contain strong predictive information toward classifying the disposition of patients for certain medical complaints, such as altered consciousness or stroke. Additionally, we show that the document-topic vectors generated by our method can be used as features to further improve classification accuracy by up to 1% across different medical complaints, for example, 74.3%–75.3% accuracy for patients with stroke symptoms. This improvement could be clinically impactful for certain patients, especially when the scale of hospital patients is large. Furthermore, the generated word-topic vectors provide a bi-clustering interpretation under each topic due to the orthogonal formulation, which can be beneficial for hospitals in better understanding the symptoms and reasons behind patients’ visits. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13368924.v1
title: Topic Modeling on Triage Notes with Semi-orthogonal Non-negative Matrix Factorization

description: Emergency Department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-bed for a patient is delayed until a doctor makes an admission decision. In this case study, we build a classifier to predict the disposition of patients using manually typed nurse notes collected during triage as provided by the Alberta Medical Center. These predictions can potentially be incorporated to early bed coordination and fast track streaming strategies to alleviate overcrowding and waiting times in the ED. However, these triage notes involve high dimensional, noisy, and sparse text data, which make model-fitting and interpretation difficult. To address this issue, we propose a novel semi-orthogonal non-negative matrix factorization (SONMF) for both continuous and binary predictors to reduce the dimensionality and derive word topics. The triage notes can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. Our real data analysis shows that the triage notes contain strong predictive information towards classifying the disposition of patients for certain medical complaints, such as altered consciousness or stroke. Additionally, we show that the document-topic vectors generated by our method can be used as features to further improve classification accuracy by up to 1% across different medical complaints, i.e. 74.3% to 75.3% accuracy for patients with stroke symptoms. This improvement could be clinically impactful for certain patients, especially when the scale of hospital patients is large. Furthermore, the generated word-topic vectors provide a bi-clustering interpretation under each topic due to the orthogonal formulation, which can be beneficial for hospitals in better understanding the symptoms and reasons behind patients’ visits.
DOI: 10.6084/m9.figshare.12100701
title: Integrating Multisource Block-Wise Missing Data in Model Selection

description: For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12100701.v2
title: Integrating Multisource Block-Wise Missing Data in Model Selection

description: For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16632569
title: Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation

description: The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pretreatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve tradeoffs between interpolation and extrapolation bias. We use a series of empirically based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we apply these estimators to examine the economic costs of conflicts in the context of Spain.
DOI: 10.6084/m9.figshare.16540322
title: A Penalized Synthetic Control Estimator for Disaggregated Data

description: Synthetic control methods are commonly applied in empirical research to estimate the effects of treatments or interventions on aggregate outcomes. A synthetic control estimator compares the outcome of a treated unit to the outcome of a weighted average of untreated units that best resembles the characteristics of the treated unit before the intervention. When disaggregated data are available, constructing separate synthetic controls for each treated unit may help avoid interpolation biases. However, the problem of finding a synthetic control that best reproduces the characteristics of a treated unit may not have a unique solution. Multiplicity of solutions is a particularly daunting challenge when the data include many treated and untreated units. To address this challenge, we propose a synthetic control estimator that penalizes the pairwise discrepancies between the characteristics of the treated units and the characteristics of the units that contribute to their synthetic controls. The penalization parameter trades off pairwise matching discrepancies with respect to the characteristics of each unit in the synthetic control against matching discrepancies with respect to the characteristics of the synthetic control unit as a whole. We study the properties of this estimator and propose data-driven choices of the penalization parameter.
DOI: 10.6084/m9.figshare.16540322.v2
title: A Penalized Synthetic Control Estimator for Disaggregated Data

description: Synthetic control methods are commonly applied in empirical research to estimate the effects of treatments or interventions on aggregate outcomes. A synthetic control estimator compares the outcome of a treated unit to the outcome of a weighted average of untreated units that best resembles the characteristics of the treated unit before the intervention. When disaggregated data are available, constructing separate synthetic controls for each treated unit may help avoid interpolation biases. However, the problem of finding a synthetic control that best reproduces the characteristics of a treated unit may not have a unique solution. Multiplicity of solutions is a particularly daunting challenge when the data include many treated and untreated units. To address this challenge, we propose a synthetic control estimator that penalizes the pairwise discrepancies between the characteristics of the treated units and the characteristics of the units that contribute to their synthetic controls. The penalization parameter trades off pairwise matching discrepancies with respect to the characteristics of each unit in the synthetic control against matching discrepancies with respect to the characteristics of the synthetic control unit as a whole. We study the properties of this estimator and propose data-driven choices of the penalization parameter.
DOI: 10.6084/m9.figshare.16632569.v2
title: Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation

description: The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pretreatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve tradeoffs between interpolation and extrapolation bias. We use a series of empirically based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we apply these estimators to examine the economic costs of conflicts in the context of Spain.
DOI: 10.6084/m9.figshare.15135426.v3
title: On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls

description: We consider the asymptotic properties of the synthetic control (SC) estimator when both the number of pretreatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the SC unit asymptotically recovers the factor structure of the treated unit, even when the pretreatment fit is imperfect. This happens when there are weights diluted among an increasing number of control units such that a weighted average of the factor structure of the control units asymptotically reconstructs the factor structure of the treated unit. In this case, the SC estimator is asymptotically unbiased even when treatment assignment is correlated with time-varying unobservables. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.