Replication Data Research Articles

Simulated redistricting plans for the analysis and evaluation of redistricting in the United States

This article introduces the 50stateSimulations, a collection of simulated congressional districting plans and underlying code developed by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. The 50stateSimulations allow for the evaluation of enacted and other congressional redistricting plans in the United States. While the use of redistricting simulation algorithms has become standard in academic research and court cases, any simulation analysis requires non-trivial efforts to combine multiple data sets, identify state-specific redistricting criteria, implement complex simulation algorithms, and summarize and visualize simulation outputs. We have developed a complete workflow that facilitates this entire process of simulation-based redistricting analysis for the congressional districts of all 50 states. The resulting 50stateSimulations include ensembles of simulated 2020 congressional redistricting plans and necessary replication data. We also provide the underlying code, which serves as a template for customized analyses. All data and code are free and publicly available. This article details the design, creation, and validation of the data.

Goodness-of-Fit Tests in Calibration: Are They Any Good for Selecting Least-Squares Weighting Formulas?

No. Most goodness-of-fit (GOF) tests attempt to discern a preferred weighting using either absolute or relative errors in the back-calculated calibration x values. However, the former are predisposed to select constant weighting and the latter 1/x2 or 1/y2 weighting, no matter what the true weighting should be. Here, I use Monte Carlo simulations to quantify the flaws in GOF tests and show why they falsely prefer their predisposition weighting. The weighting problem is solved properly through variance function (VF) estimation from replicate data, conveniently separating this from the problem of selecting a response function (RF). Any weighting other than inverse-variance must give loss of precision in the RF parameters and in the estimates of unknowns x0. In particular, the widely used 1/x2 weighting, if wrong, not only sacrifices precision but even worse, appears to give better precision at small x, leading to falsely optimistic estimates of detection and quantification limits. Realistic VFs typically become constant in the low-x, low-y limit. Thus, even when 1/x2 weighting is correct at large signal, the neglect of the constant variance component at small signal again gives too-small detection and quantification limits. VF estimation has been disparaged as too demanding of data. Why this is not true is demonstrated with Monte Carlo simulations that show only a few percent increase in calibration parameter uncertainties when the VF is estimated from just three replicates at each of six calibration x values. This point is further demonstrated using examples from the recent literature.

Synthetic difference-in-differences estimation with staggered treatment timing

This note formalizes the synthetic difference-in-differences estimator for staggered treatment adoption settings, as briefly described in Arkhangelsky et al. (2021). To illustrate the importance of this estimator, I use replication data from Abrams (2012). I compare the estimators obtained using SynthDiD, TWFE, the group time average treatment effect estimator of Callaway and Sant’Anna (2021), and the partially pooled synthetic control method estimator of Ben-Michael et al. (2021) in a staggered treatment adoption setting. I find that in this staggered treatment setting, SynthDiD provides a numerically different estimate of the average treatment effect. Simulation results show that these differences may be attributable to the underlying data generating process more closely mirroring that of the latent factor model assumed for SynthDiD than that of additive fixed effects assumed under traditional difference-in-differences frameworks.

Automated replication of tuple spaces via static analysis

• Data replication may improve the efficiency of a distributed system. • Implementing replication manually requires a significant effort. • We propose an experimental technique for automated replication of tuple spaces. • We combine program transformation and static analysis, preserving strong consistency. • Transformed programs exhibit desirable properties (e.g. reduced non-local accesses). Coordination languages for tuple spaces can offer significant advantages in the specification and implementation of distributed systems, but often do require manual programming effort to ensure consistency. We propose an experimental technique for automated replication of tuple spaces in distributed systems. The system of interest is modelled as a concurrent Go program where different threads represent the behaviour of the separate components, each owning its own local tuple repository. We automatically transform the initial program by combining program transformation and static analysis, so that tuples are replicated depending on the components' read-write access patterns. In this way, we turn the initial system into a replicated one where the replication of tuples is automatically achieved, while avoiding unnecessary replication overhead. Custom static analyses may be plugged in easily in our prototype implementation. We see this as a first step towards developing a fully-fledged framework to support designers to quickly evaluate many classes of replication-based systems under different consistency levels.

An automate failure recovery for synchronous distributed database system

Periodically, researchers have been sharing their constant attempts to improve the existing methods for data replication in distributed database system. The main goal is to work for an efficient distributed environment. An efficient environment may handle huge amount of data and preserve data availability. The occasionally failures in distributed systems will affect the end results, such as data loss, income loss etc. Thus, to prevent the data loss and guarantee the continuity of the business, many organizations have applied disaster recovery solutions in their system. One of the widely used is database replication, because it guarantees data safety and availability. However, disaster still can occur in database replication. Hence, an automatic failure recovery technique called distributed database replication with fault tolerance (DDR-FT) has been proposed in this research. DDR-FT uses heartbeat message for node monitoring. Subsequently, a foundation of binary vote assignment for fragmented database (BVAFD) replication technique has been used. In DDR-FT, the data nodes are continuously monitored while auto reconfiguring for automatic failure recovery. From the conducted experiments, it is proved that DDR-FT can preserve system availability. It shows that DDR-FT technique provides a convenient approach to system availability for distributed database replication in real time environment.

HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force.

Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The overarching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.

Cost-based Optimization of Multistore Query Plans

Multistores are data management systems that enable query processing across different and heterogeneous databases; besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. Our multistore solution relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by formulating and evaluating different execution plans on the multistore. In particular, we outline different strategies to carry out joins and data fusion by relying on different schema representations; then, a self-learning black-box cost model is used to estimate execution times and select the most efficient plan. The experiments assess the effectiveness of the cost model in choosing the best execution plan for the given queries and exploit multiple multistore benchmarks to investigate the factors that influence the performance of different plans.

Verifiable Delay Function and Its Blockchain-Related Application: A Survey.

The concept of verifiable delay functions has received attention from researchers since it was first proposed in 2018. The applications of verifiable delay are also widespread in blockchain research, such as: computational timestamping, public random beacons, resource-efficient blockchains, and proofs of data replication. This paper introduces the concept of verifiable delay functions and systematically summarizes the types of verifiable delay functions. Firstly, the description and characteristics of verifiable delay functions are given, and weak verifiable delay functions, incremental verifiable delay functions, decodable verifiable delay functions, and trapdoor verifiable delay functions are introduced respectively. The construction of verifiable delay functions generally relies on two security assumptions: algebraic assumption or structural assumption. Then, the security assumptions of two different verifiable delay functions are described based on cryptography theory. Secondly, a post-quantum verifiable delay function based on super-singular isogeny is introduced. Finally, the paper summarizes the blockchain-related applications of verifiable delay functions.

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force

ObjectivesAmbiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. MethodsThe International Society for Pharmacoepidemiology (ISPE) and ISPOR–The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. Strategies to Disseminate and Facilitate UseRecognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. ConclusionThe HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.

Data Virtualization Strategies –Next Frontier for Business Analytics

Data virtualization is a critical strategy that organizations employ to enhance business analytics in the age of Big Data. This approach allows for seamless access to and integration of data from diverse sources, both internal and external, without the need for physical data movement or replication. This paper explores data virtualization strategies and their impact on business analytics, highlighting the benefits, challenges, and best practices associated with this technology. By adopting data virtualization, organizations can achieve a unified and real-time view of their data, enabling faster and more informed decisionmaking in a rapidly evolving business landscape.

Subgroups of internet gaming disorder based on addiction-related resting-state functional connectivity.

To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Zhejiang province and Beijing, China. One hundred and sixty-nine IGD and 147 control subjects. k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures. Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks. There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.

Finite Mixtures of Multivariate Wrapped Normal Distributions for Model Based Clustering of p-Torus Data

We consider a finite mixture model of multivariate Wrapped Normal distributions to handle non homogeneous circular data on a p-dimensional torus ( p ≥ 2 ). The Wrapped Normal distribution is a valid alternative to model multivariate circular or directional data on a p-torus. Parameter estimation is carried out through a nested (classification) EM algorithm, by exploiting the ideas of unwrapping circular data. The source of incompleteness in the outer E-step is represented by unobserved group memberships, whereas the source of incompleteness in the inner E-step is given by the unobserved vectors of wrapping coefficients. The finite sample behavior of the proposed method has been investigated by Monte Carlo numerical studies and real data examples. Supplemental materials for the article, including data and R codes for implementing methods, running simulations and replicate data analyses, are available online.

Replication Data for: Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models

Replication Data for: Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models

Replication Data for: Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models

Replication Data for: Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models

Quantifying the impact of data replication on error propagation

Quantifying the impact of data replication on error propagation

Seasonal bacterial niche structures and chemolithoautotrophic ecotypes in a North Atlantic fjord

Quantifying the temporal change of bacterial communities is essential to understanding how both natural and anthropogenic pressures impact the functions of coastal marine ecosystems. Here we use weekly microbial DNA sampling across four years to show that bacterial phyla have distinct seasonal niches, with a richness peak in winter (i.e., an inverse relationship with daylength). Our results suggest that seasonal fluctuations, rather than the kinetic energy or resource hypotheses, dominated the pattern of bacterial diversity. These findings supplement those from global analyses which lack temporal replication and present few data from winter months in polar and temperate regions. Centered log-ratio transformed data provided new insights into the seasonal niche partitioning of conditionally rare phyla, such as Modulibacteria, Verrucomicrobiota, Synergistota, Deinococcota, and Fermentibacterota. These patterns could not be identified using the standard practice of ASV generation followed by rarefaction. Our study provides evidence that five globally relevant ecotypes of chemolithoautotrophic bacteria from the SUP05 lineage comprise a significant functional group with varying seasonal dominance patterns in the Bedford Basin.

A literature review and database of how the primary KIT/PDGFRA variant of a gastrointestinal stromal tumour predicts for sensitivity to imatinib

It is well recognized that the primary KIT or PDGFRA variant of a gastrointestinal stromal tumour (GIST) can predict sensitivity to imatinib. However, these data are currently spread across a wide range of publications and have not been collated as one reference. A broad-ranging literature search was therefore performed to assemble such a database which should help optimize imatinib-based management of GIST patients henceforth. Having excluded wild type GISTs and results for imatinib used as adjuvant therapy, 79 publications (dated August 2001 to March 2022) underwent data extraction. These data on imatinib sensitivity were either derived from in vitro studies, predicted by in silico analysis or based on in vivo clinical patient response. Data interpretation carried some caveats: there was a potential for replication of patient-derived data between older and new publications; only predicted protein sequences were presented; the criteria used to record clinical response were not uniform across all publications; and imatinib dosage could vary between different clinical publications. However, these data showed broad agreement of imatinib sensitivity amongst similar subtypes of KIT or PDGFRA variant. There was also agreement between in vivo versus in vitro/in silico derived sensitivity data for most variants when both data types were available.

Can we have a second helping? A preregistered direct replication study on the neurobiological mechanisms underlying self-control.

Self‐control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self‐control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self‐control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow‐up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self‐control and it correlated with both taste and health in self‐controllers but only taste in non‐self‐controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self‐control. The absence of statistically significant group differences in dlPFC activity during successful self‐control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer‐term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region‐of‐interest analysis.

Decentralized Moving Target Defense for Microgrid Protection Against False-Data Injection Attacks

This paper proposes the Decentralized Moving Target Defense via Data Replication (DMTDR) framework, which increases the security of microgrids by adding two layers of uncertainty that limit the success of false-data injection attacks. The DMTDR framework exploits the scalability and low cost of IoT devices to replicate relevant sensory and control signals, which are randomly transmitted through independent communication channels. Then, one of the transmitted replicas is randomly selected to reach its intended destinations. As opposed to most moving target defense (MTD) approaches found in the literature, the proposed DMTDR does not require central coordination but instead it can be applied to any monitoring or control device that utilizes a communication channel. Furthermore, the proposed approach does not deteriorate the system performance in normal operation. The theoretical foundations for the optimal allocation of replicas per signal are developed, and fundamental limits of uncertainties introduced by the framework are calculated. Moreover, taking advantage of the replicated information, a decentralized attack detection strategy is introduced, which is able to detect the presence of malicious data by only using local information without requiring the computation of any estimation model. The proposed framework is demonstrated on a test microgrid, where the data replication and random transmission are implemented on a WiFi network with IoT devices. The results show that the proposed framework considerably improves: i) security by limiting the impact of false-data injection attacks; ii) detectability by enabling the detection of stealthy attacks.

Replication Data for: Scalings for eddy buoyancy fluxes across prograde shelf/slope fronts

Replication Data for: Scalings for eddy buoyancy fluxes across prograde shelf/slope fronts