scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

scholarly journals An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

2011 ◽  
Vol 68 (10) ◽  
pp. 2252-2263 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Youen Vermard ◽  
Trevor Hutton ◽  
Ane Iriondo ◽  
Angélique Jadaud ◽  
...  
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Technical Information ◽  
Fishing Effort ◽  
Additive Models ◽  
Induced Variation ◽  
Fishing Fleets ◽  
The Impact ◽  
Selection Of ◽  
Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

scholarly journals Percolation models of pathogen spillover

2019 ◽  
Vol 374 (1782) ◽  
pp. 20180331 ◽  
Author(s):  
Alex D. Washburne ◽  
Daniel E. Crowley ◽  
Daniel J. Becker ◽  
Kezia R. Manlove ◽  
Marissa L. Childs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Mechanistic Model ◽  
Piecewise Linear ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Management Intervention ◽  
Management Concepts ◽  
The Impact ◽  
Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models

2021 ◽  
pp. 1-10
Author(s):  
Hanna M. van Loo ◽  
Lian Beijers ◽  
Martijn Wieling ◽  
Trynke R. de Jong ◽  
Robert A. Schoevers ◽  
...  
Keyword(s):  
Internalizing Symptoms ◽  
Nonlinear Models ◽  
Generalized Additive Models ◽  
Population Sample ◽  
Internalizing Disorders ◽  
Relative Difference ◽  
Additive Models ◽  
Point Prevalence ◽  
Cross Sectional ◽  
The Impact

Abstract Background Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. Methods We used cross-sectional data on 146 315 subjects, aged 18–80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders – major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder – were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. Results The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). Conclusions The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

Library Impact Research Report: Understanding and Communicating Research Impact: The Needs of STEM and Health Sciences Faculty and Postdoctoral Researchers

2021 ◽  
Author(s):  
Negeen Aghassibake ◽  
Lynly Beard ◽  
Jackie Belanger ◽  
Diana Louden ◽  
Robin Chin Roemer ◽  
...  
Keyword(s):  
Research Impact ◽  
Health Sciences ◽  
Future Research ◽  
Research Report ◽  
Library Impact ◽  
The University ◽  
Postdoctoral Researchers ◽  
Impact Framework ◽  
The Impact

As part of ARL’s Research Library Impact Framework initiative, the University of Washington (UW) Libraries explored UW faculty and postdoctoral researcher needs for understanding and communicating the impact of their work, with a focus on researchers in science, technology, engineering, and math (STEM) and health sciences fields. The project was designed to understand the challenges researchers face in this area, identify how participants in these fields define and measure impact, and explore their priorities for research-impact support. The project team conducted a survey and follow-up interviews to investigate these questions. This research report presents the project team’s methodology, findings, and recommendations for future research.

scholarly journals Impact of Moderate to Severe Renal Impairment on Mortality and Appropriate Shocks in Patients with Implantable Cardioverter Defibrillators

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Venkata M. Alla ◽  
Kishlay Anand ◽  
Mandeep Hundal ◽  
Aimin Chen ◽  
Showri Karnam ◽  
...  
Keyword(s):  
Linear Models ◽  
Severe Renal Impairment ◽  
Medical Center ◽  
Implantable Cardioverter Defibrillators ◽  
Free Survival ◽  
Kaplan Meier ◽  
Appropriate Shocks ◽  
The Impact ◽  
Cardioverter Defibrillators

Background. Due to underrepresentation of patients with chronic kidney disease (CKD) in large Implantable-Cardioverter Defibrillator (ICD) clinical trials, the impact of ICD remains uncertain in this population.Methods. Consecutive patients who received ICD at Creighton university medical center between years 2000–2004 were included in a retrospective cohort after excluding those on maintenance dialysis. Based on baseline Glomerular filtration rate (GFR), patients were classified as severe CKD: GFR < 30 mL/min; moderate CKD: GFR: 30–59 mL/min; and mild or no CKD: GFR ≥ 60 mL/min. The impact of GFR on appropriate shocks and survival was assessed using Kaplan-Meier method and Generalized Linear Models (GLM) with log-link function.Results. There were 509 patients with a mean follow-up of 3.0 + 1.3 years. Mortality risk was inversely proportional to the estimated GFR: 2 fold higher risk with GFR between 30–59 mL/min and 5 fold higher risk with GFR < 30 mL/min. One hundred and seventy-seven patients received appropriate shock(s); appropriate shock-free survival was lower in patients with severe CKD (GFR < 30) compared to mild or no CKD group (2.8 versus 4.2 yrs).Conclusion. Even moderate renal dysfunction increases all cause mortality in CKD patients with ICD. Severe but not moderate CKD is an independent predictor for time to first appropriate shock.

Improvement of Acupuncture Therapy on Relapse of P atients with G outy A rthritis: A Pairwise and Bayesian Network Meta analysis

2021 ◽  
Author(s):  
Zhihui Li ◽  
Min Chen ◽  
Chunzhi Tang
Keyword(s):  
Acupuncture Therapy ◽  
Bayesian Network ◽  
Meta Analysis ◽  
Gouty Arthritis ◽  
Serum Urate ◽  
Cochrane Library ◽  
Future Research ◽  
Fire Needle ◽  
The Impact

Objective: The aim of this study is to investigate the impact of acupuncturetherapy on relapse of patients with gouty arthritis (GA). Methods: “gout ORgouty arthritis” AND “a cupuncture therapy OR acupuncture OR moxibustionOR electroacupuncture OR fire needle OR acupotomology OR blood lettingpuncture OR plum blossom needle” were used as search strategies forsearching related studies. Twenty two studies involving 2394 patient s wereenrolled in this research through the analysis of databases of CNKI, Wanfang,VIP, PubMed, Embase and Cochrane Library. Results: The results of pairwise metaanalysis and network meta analysis (NMA) indicated that patients withacupuncture therapy had a significantly lower relapse rate (RR) compared withthose without acupuncture therapy (OR = 0.21, 95% CI: 0.16 0.26, P <0.00001); the follow up time (TFU) and serum urate concentration (SUA)before treatment had no significant effect on the reductio n of RR caused byacupuncture therapy (P > 0.05); and patients treated with acupuncture plusWestern medicine (WM) had the lowest RR (surface under the cumulativeranking [SUCRA] = 85.0%), followed by acupuncture plus traditional Chinesemedicine (TCM, SUC RA = 73.5%), acupuncture only (SUCRA = 72.8%),fourthly acupuncture plus TCM and WM (SUCRA = 33.0%), then TCM(SUCRA = 28.7%), finally WM (SUCRA = 7.0%). Conclusion: Our findingmay facilitate the application of acupuncture therapy in patients with GA. Ourresearch also offered some information for the future research.

scholarly journals A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 91
Author(s):  
Jean-Philippe Boucher ◽  
Roxane Turcotte
Keyword(s):  
Poisson Distribution ◽  
Fixed Effects ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Panel Count Data ◽  
Insurance Company ◽  
Smooth Functions ◽  
Claim Frequency ◽  
The Impact ◽  
The Relationship

Using telematics data, we study the relationship between claim frequency and distance driven through different models by observing smooth functions. We used Generalized Additive Models (GAM) for a Poisson distribution, and Generalized Additive Models for Location, Scale, and Shape (GAMLSS) that we generalize for panel count data. To correctly observe the relationship between distance driven and claim frequency, we show that a Poisson distribution with fixed effects should be used because it removes residual heterogeneity that was incorrectly captured by previous models based on GAM and GAMLSS theory. We show that an approximately linear relationship between distance driven and claim frequency can be derived. We argue that this approach can be used to compute the premium surcharge for additional kilometers the insured wants to drive, or as the basis to construct Pay-as-you-drive (PAYD) insurance for self-service vehicles. All models are illustrated using data from a major Canadian insurance company.

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