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Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain-and potentially transform-memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18-40 years; Experiment 2: N = 204, age range = 19-40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

Countless policies are crafted with the intention of punishing all who do wrong or rewarding only those who do right. However, this requires accommodating certain mistakes: some who do not deserve to be punished might be, and some who deserve to be rewarded might not be. Six preregistered experiments (N = 3,484 U.S. adults) reveal that people are more willing to accept this trade-off in principle, before errors occur, than in practice, after errors occur. The result is an asymmetry such that for punishments, people believe it is more important to prevent false negatives (e.g., criminals escaping justice) than to fix them, and more important to fix false positives (e.g., wrongful convictions) than to prevent them. For rewards, people believe it is more important to prevent false positives (e.g., welfare fraud) than to fix them and more important to fix false negatives (e.g., improperly denied benefits) than to prevent them.

This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.

Open research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data. [ABSTRACT FROM AUTHOR]

The uniqueness of the European Parliament, as well as the magnitude of impact its decisions wield over member states, are elements that capture researchers' attention. However, several of this institution's particularities have made broad analysis of the textual content it produces difficult. This research note presents Vitrine Démocratique, a new, publicly accessible, and centralized database structuring interventions made in the European Parliament starting in 2014, both in their original languages and translated to English. The process by which this high-velocity database was created is presented, as well as a descriptive overview of the contents of this data source, which is continuously updated on a daily basis. [ABSTRACT FROM AUTHOR]

This article critically examines the domination exerted by big digital companies on the current social, economic, and political context of modern societies, with a particular focus on the implications for the proper functioning of democracy. The objective of this article is to introduce and develop the concept of datafeudalism, expose its emergence for the proper functioning of modern societies and democracy, and to propose courses of action to reverse this situation. To achieve this purpose, firstly, the evolution from surveillance capitalism to datafeudalism will be discussed. Secondly, the structures and operating logic of data feudalism will be analyzed. Thirdly, the harmful impacts of datafeudalism on the proper functioning of the democratic systems of the European Union will be examined. Finally, an attempt will be made to outline courses of action that will make it possible to reverse the situation of economic, social and political tyranny exercised by big digital companies through datafeudalism. [ABSTRACT FROM AUTHOR]

Purpose: T1 mapping is a widely used quantitative MRI technique, but its tissue‐specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well‐established inversion‐recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open‐source platforms. Intersubmission and intrasubmission comparisons were performed. Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. Conclusion: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo. [ABSTRACT FROM AUTHOR]

By implementing a novel synthetic difference-in-differences approach, this article assesses whether adopting open data policy will increase the impact of a journal. Using China Industrial Economics (hereafter CIE, a leading Chinese journal which mandates authors to open their research data/code in the end of 2016) as a quasi-experiment, we show that the introduction of open data policy leads to an increase in CIE's impact factor by a factor of approximately 1.5 to 2 times. [ABSTRACT FROM AUTHOR]

IMPACT: To foster data-driven policy-making practices there is a need for capacity-building within the public administration (PA) to collect, access, manage and reuse data. This article draws from focus groups and interviews to propose measures to make dataspaces scalable and accessible for smaller actors so that they can participate in data-driven policy-making—both in terms of their capacity to input data in a dataspace and to reuse existing data. This article speaks to PA and data governance scholars, as well as PA and data management practitioners—particularly at the local level in Europe. This article assesses the obstacles and best practices in data gathering and reuse for public administrations (PAs). Through a qualitative analysis, it finds that smaller PAs often lack the resources to harness data in policy-making. This means that data-driven policy-making is currently more applicable to larger organizations. Theoretically, this article critically challenges the idea that pooling data from various sources expands the boundaries of rationality through collective rationality, as several institutional actors remain excluded from data-driven policy-making due to lack of resources. [ABSTRACT FROM AUTHOR]

Patents are essential for transferring scientific discoveries tomeaningful products that benefit societies. While the academic community focuses on the number of citations to rank scholarly works according to their "scientific merit," the number of citations is unrelated to the relevance for patentable innovation. To explore associations between patents and scholarly works in publicly available patent data, we propose to utilize statistical methods that are commonly used in biology to determine gene-disease associations. We illustrate their usage on patents related to biotechnological trends of high relevance for food safety and ecology, namely the CRISPR-based gene editing technology (>60,000 patents) and cyanobacterial biotechnology (>33,000 patents). Innovation trends are found through their unexpected large changes of patent numbers in a time-series analysis. From the total set of scholarly works referenced by all investigated patents (~254,000 publications), we identified ~1,000 scholarly works that are statistical significantly over-represented in the references of patents from changing innovation trends that concern immunology, agricultural plant genomics, and biotechnological engineering methods. The detected associations are consistent with the technical requirements of the respective innovations. In summary, the presented data-driven analysis workflow can identify scholarly works that were required for changes in innovation trends, and, therefore, is of interest for researches that would like to evaluate the relevance of publications beyond the number of citations. [ABSTRACT FROM AUTHOR]

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low‐impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open‐access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation. [ABSTRACT FROM AUTHOR]

Determining the manipulation unit of working memory is one of the fundamental questions in understanding how working memory functions. The prevalent object-based theory in cognitive research predicts that memory manipulation is performed on the level of objects. Here we show instead that the basic units of working memory manipulation are Boolean maps, a data structure describing what can be perceived in an instant. We developed four new manipulation tasks (with data from 80 adults) and showed that manipulation times only increased when the number of Boolean maps manipulated increased. Increasing the number of orientations manipulated did not induce longer manipulation times, consistent with a key prediction of the Boolean map theory. Our results show that Boolean maps are the manipulation unit of working memory. [ABSTRACT FROM AUTHOR]

Across development, people tend to demonstrate a preference for contexts in which they have the opportunity to make choices. However, it is not clear how children, adolescents, and adults learn to calibrate this preference based on the costs and benefits of agentic choice. Here, in both a primary, in-person, reinforcement-learning experiment (N = 92; age range = 10–25 years) and a preregistered online replication study (N = 150; age range = 8–25 years), we found that participants overvalued agentic choice but also calibrated their agency decisions to the reward structure of the environment, increasingly selecting agentic choice when choice had greater instrumental value. Regression analyses and computational modeling of participant choices revealed that participants' bias toward agentic choice—reflecting its intrinsic value—remained consistent across age, whereas sensitivity to the instrumental value of agentic choice increased from childhood to early adulthood. [ABSTRACT FROM AUTHOR]

Textbook psychology holds that people usually prefer a certain option over a risky one when options are framed as gains but prefer the opposite when options are framed as losses. However, this pattern can be amplified, eliminated, or reversed depending on whether option descriptions include only positive information (e.g., "200 people will be saved"), only negative information (e.g., "400 people will not be saved"), or both. Previous studies suggest that framing effects arise only when option descriptions are mismatched across frames. Using online and student samples (N s = 906 and 521), we investigated 81 framing-effect variants created from matched and mismatched pairs of 18 option descriptions (nine in each frame). Description valence or gist explained substantial variation in risk preferences (prospect theory does not predict such variation), but a considerable framing effect remained in our balanced design. Risky-choice framing effects appear to be partly—but not completely—the result of mismatched comparisons. [ABSTRACT FROM AUTHOR]

The aggregation of many lay judgments generates surprisingly accurate estimates. This phenomenon, called the "wisdom of crowds," has been demonstrated in domains such as medical decision-making and financial forecasting. Previous research identified two factors driving this effect: the accuracy of individual assessments and the diversity of opinions. Most available strategies to enhance the wisdom of crowds have focused on improving individual accuracy while neglecting the potential of increasing opinion diversity. Here, we study a complementary approach to reduce collective error by promoting erroneous divergent opinions. This strategy proposes to anchor half of the crowd to a small value and the other half to a large value before eliciting and averaging all estimates. Consistent with our mathematical modeling, four experiments (N = 1,362 adults) demonstrated that this method is effective for estimation and forecasting tasks. Beyond the practical implications, these findings offer new theoretical insights into the epistemic value of collective decision-making. [ABSTRACT FROM AUTHOR]

Our understanding of ownership influences how we interact with objects and with each other. Here, we studied people's intuitions about ownership transfer using a set of simple, parametrically varied events. We found that people (N = 120 U.S. adults) had similar intuitions about ownership for some events but sharply opposing intuitions for others (Experiment 1). People (N = 120 U.S. adults) were unaware of these conflicts and overestimated ownership consensus (Experiment 2). Moreover, differences in people's ownership intuitions predicted their intuitions about the acceptability of using, altering, controlling, and destroying the owned object (N = 130 U.S. adults; Experiment 3), even when ownership was not explicitly mentioned (N = 130 U.S. adults; Experiment 4). Subject-level analyses suggest that these disagreements reflect at least two underlying intuitive theories, one in which intentions are central to ownership and another in which physical possession is prioritized. [ABSTRACT FROM AUTHOR]

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis. [ABSTRACT FROM AUTHOR]

As one of the most popular sources of information in the world, Wikipedia is edited by a large, global community of contributors. User-generated nature of this online encyclopedia ensures that the information reflects a wide range of topics. Hovewer, Wikipedia articles are created and edited independently in each language version. Therefore, some topics may be presented with varying degrees of completeness depending on their importance in a particular language community. In this paper, we quantified the concept of Americanization on a global scale through comparative analysis of the coverage of American topics in different language versions of Wikipedia. For this purpose, we analyzed over 90 million Wikidata items and 40 million Wikipedia articles in 58 languages. We discussed whether Americanization is more or less dominant in different languages, regions, and cultures. We showed that the interest in American topics is not universal. Western, developed countries are more Americanized (more interested in topics related to America) than the rest of the world. This is the first global, quantitative confirmation of issues often hypothesized, or assumed, in the literature on Americanization and related phenomena. This study shows that Wikipedia and Wikidata can allow quantification of social science concepts that previously were considered not realistically measurable. Finally, the presented research is also relevant to the discourses on the biases of Wikipedia. [ABSTRACT FROM AUTHOR]

Open research is an increasingly developed and crucial framework for the advancement of science and has seen successful adoption across a broad range of disciplines. Entomology has, however, been slow to adopt these practices compared to many adjacent fields despite ethical and practical imperatives to do so.The grand challenges facing entomology in the 21st century require the synthesis of evidence at global scales, necessitating open sharing of data and research at a pace and scale incompatible with the slow adoption of open research practices. Open science also plays a vital role in fostering trust in research and maximizing use of research outputs, which is ethically crucial for reducing harms to insects.We outline these imperatives and how open research practices can enhance entomological research across a range of contexts. We also highlight the holistic nature of open science across the full research lifecycle through several specific examples of open research practices, which can be adopted easily by individual entomologists.We do, however, argue that the responsibility of promoting, integrating and encouraging open research is most crucially held by publishers, including scholarly societies, which have leveraged widespread adoption in adjacent fields. Entomology must advance quickly to become a leading discipline in the open research transition. [ABSTRACT FROM AUTHOR]

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock. [ABSTRACT FROM AUTHOR]

The current increase in the number of large, open sets of unstructured textual data has created both opportunities and challenges for social scientists. Here, we explore if and how we can use such data by looking at a dataset of over 144,000 documents used by parliamentary committees in Sweden. Of these, we aim to understand: (a) the topical content of these motions, (b) how these topics have changed over time, and (c) how these topics differ across political parties. To do so, we use a Structural Topic Model, which allows us to not only find the topics using the textual data itself, but also to include the documents' metadata, such as authorship and date of publication. Doing so, we find 30 topics, which we combine into 9 broader themes. We find that these themes often rise and fall in popularity in line with historical events, and relate to the various political parties as we would expect. Throughout our analysis, we provide a step-by-step overview of how to use structural topic models in practice and also how to handle the type of dataset we use here. [ABSTRACT FROM AUTHOR]

Modern research often involves the collection or analysis of data and the use of specialized computer algorithms. Traditional text articles thus provide only partial documentation of a research study. Readers have limited ability to reproduce or utilize work if the source data are not available or if it relies on an algorithm that is described, but code is not provided. Fortunately, a wide variety of tools are now available to support the publication of research data and code. The effort required to publish data is now relatively small, and the benefits can be immense. This opinion article discusses trends toward increased sharing in academic publishing. It describes opportunities and resources to support data and code sharing and describes the benefits for both authors and readers. Finally, it discusses how Earthquake Spectra is providing resources and enhancing its policies to establish the sharing of data as the default procedure when publishing in the journal, and encourage the sharing of code and other resources. [ABSTRACT FROM AUTHOR]

Background: Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient's length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper. Methods: We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns. Results: The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns. Conclusion: Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery. [ABSTRACT FROM AUTHOR]

Scientific evaluation of the transportation infrastructure development level of the planning area in the territorial spatial planning is of practical significance to improve the planning science. Using low-cost open data and starting from the essence of transportation, an evaluation model consisting of four factors was constructed to conduct a comprehensive transportation advantage study in the land spatial planning of Diqing Tibetan Autonomous Prefecture, Yunnan. The results show that the use of open data can reduce the difficulty of model application and improve the precision and reliability of evaluation. The evaluation shows that the degree of comprehensive transportation superiority of Diqing Tibetan Autonomous Prefecture coincides with the characteristics of its natural geospatial distribution, and it is obviously distributed in the form of a “ belt”, so the degree of advantage of the Lancang River belt should be given priority to be improved in the future. Based on the data analysis, it is beneficial to “shape by flow” in the layout. The analysis shows that the lack of fast road system is the biggest short board on the transportation supply side of Diqing Tibetan Autonomous Prefecture. In the urban network, its advantage in external transportation in Tibet and Sichuan is weak, and the channel construction needs to be strengthened urgently. The research can provide reference value for digital intelligence to realize the integrated development of transportation system and “three living spaces” and to apply the theory of comprehensive transportation superiority in territorial spatial planning at prefecture and city level. [ABSTRACT FROM AUTHOR]

The EU aims to drive the data and platform economy by establishing a single European Data Space(s) vision, envisioning it as a catalyst for innovation, novel services, enhanced competition and consumer welfare. It involves implementing existing and forthcoming horizontal and sectorial data-sharing laws to overcome technical, legal, and ethical barriers to (personal and non-personal) data access and achieve specific data-driven policy targets. The initial spaces will cover nine economic sectors, few established and others in development. The study assesses the possible oversight of competition law collusion concerns in these initiatives, using the example of the EU Mobility Data Space (EMDS). By employing competition law qualitative doctrinal methods, it emphasizes the risks of digital collusion within these EU-wide open data frameworks, and it indicates that they could severely harm one or several parameters of competition. It concludes by offering policy recommendations to prevent or minimize collusion risks for the EMDS and suggests further research in other sectors. [ABSTRACT FROM AUTHOR]

Care, management, and statute each mandate restraint-reporting in psychiatric settings in England. PROD-ALERT in this journal ("PA1") correlated log incidence of restraint, log institutional size, and log detention. The period was September 2020 to August 2021. It showed a clear trend among reporters. Restraint correlated with institutional size and use of legal detention. Some large detaining providers reported no restraints per month despite that trend. Inference from size suggested that non-complete reporters restrained 1,774 people per month. This paper "PA2" develops analysis repeating it for September 2021 to August 2022. PA2 shows how to count L-information, i.e., questionable information, added by null reports, by applying an L-test to data sets. PA2 uses illustrative vignettes about human height to ground L-information scores from English restraint reporting. In PA2, reported restraint again correlates with size and detention as in PA1. PA2 shows evolving data. Providers still follow a trend in restraint by size and detention. Providers which newly report restraint are on trend. Inference suggests that non-complete reporters restrained 1,305 people per month (536-3233), 95% CI, a large but reduced number since PA1. English restraint data have an L-test L-information score of increase in information by a factor of L = 145. This is as surprising as claiming that an average English man of 1.72 m is 2.64 m tall. Persons restrained per month is a robust measure continuing to log-correlate with size and legal compulsion. Providers over a certain size who report null restraint probably have some. Restraint remains underreported in England. Imputation of incomplete reporters shows a large shrinking cohort of patients detained by incomplete reporters. Knowledge of this may promote reporting. Improved reporting, and the infrastructure and integrity it demands, may help providers measure and reduce restraint. PA1 remains unrefuted. L-test can measure L-information in intuitively representable ways. The informational effect of nulls on the reliable data set is similar to a claim that an average-heighted man is as tall as people with clinical gigantism. [ABSTRACT FROM AUTHOR]

As the digital transformation accelerates in our society, open data are being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: (1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? (2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (random forest, XGBoost, LightGBM, CatBoost) and multi-model (stacking ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society. [ABSTRACT FROM AUTHOR]

Green hydrogen is central to the global energy transition. This paper introduces a renewable hydrogen production system model that optimizes hydrogen production on a worldwide 50 km × 50 km grid, considering country-specific investment risks. Besides the renewable energy's impact on the hydrogen production system (HPS) design, we analyze the effect of country-specific interest rates on the levelized cost of hydrogen (LCOH) production. Over one-third (40.0%) of all cells have an installed solar PV capacity share between 50% and 70%, 76.4% have a hybrid (onshore wind and solar PV) configuration. Hydrogen storage is deployed rather than battery storage to balance hydrogen production via electrolysis and hydrogen demand. Hybrid HPSs can significantly reduce the LCOH production compared to non-hybrid designs, whereas country-specific interest rates can lead to significant increases, diminishing the relative competitiveness of countries with abundant renewable energy resources compared to countries with fewer resources but fewer investment risks. [Display omitted] • Worldwide levelized cost of hydrogen production on a 50 km × 50 km grid resolution. • Impact of country risk premiums on the levelized cost of hydrogen production. • Cost-optimal hybrid hydrogen production system design per grid cell. • All results are available on an open-data license. [ABSTRACT FROM AUTHOR]

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases. [ABSTRACT FROM AUTHOR]

The rate of discovery of near-Earth asteroids currently outpaces our ability to analyze them. Knowledge of an asteroid's physical properties is essential to deflect them. I developed free and open-source algorithms and training modules. These tools utilize images from robotic telescopes, open-source data, and high school-level mathematics to determine asteroids' size, rotation period, and strength. I took observations of the Didymos binary asteroid, and my algorithm determined its size to be 850 ± 0.04 m, with a 2.261 ± 0.018 h rotation period and rubble-pile strength. I measured a decrease in the mutual orbital period after impact by NASA's 2022 DART Mission, confirming successful deflection. My findings matched closely with those obtained by NASA and external sources (Daly et al., 2023; Thomas et al., 2023; Talbert, 2022, October 11; Nakanoet al., 2022) [1,2,3,4]. Every young person and citizen scientist can now be a planetary defender. • Rate of discovery of near-earth asteroids outpaces current abilities to analyze them. • Open data can help citizen scientists determine asteroid's physical properties. • Algorithms successfully measured success of DART Mission in deflecting Dimorphos. • Citizen scientist participation can provide valuable inputs for planetary defense considerations. [ABSTRACT FROM AUTHOR]

The ideal of unfettered data circulation has fallen into crisis. As of today, a growing number of actors are introducing measures to ensure a greater degree of control over the global data pipeline. Combining critical data studies and political theory, this article conceptualises the current technopolitical conjuncture as one of 'planetary data turbulence' in which divergences regarding the production and circulation of data have become the norm. The concept of data turbulence emerges from studies on data friction, but this article contends that the current state of affairs requires expanding the emphasis on technosciences and materiality in these works. Drawing on Ernesto Laclau and Chantal Mouffe, the article shows that attending to antagonism and discourse makes it possible to account for the eminently political forces shaping the circulation of data. The strengths of this framework are illustrated by looking at the articulation of digital sovereignty in different geographies. [ABSTRACT FROM AUTHOR]

Strategies to address the decline and marginalization of rural areas are emerging as a central issue of European and national policies. Yet, the conceptualization of decline and the recognition of forms of decline specific to distinct rural areas, resulting in conditions of intra-rural divide, are fundamental aspects for developing place-based and targeted strategies. The article thus focuses on the concept of intra-rural divide. It introduces the categories of declining, adapting, stabilizing, and developing regions as spatial clusters that present specific population trends and require specific place-based policies. These policies are defined as adaptation, mitigation, and development strategies. The aim of the study is to develop a procedure, based on a set of indicators of population trends and population structure, to investigate the impact of active decline resulting from out-migration and legacy decline resulting from demographic imbalances produced by past migration. The objective is to understand the intra-rural divide and identify clusters of declining transition and prosperous rural areas. Six Italian regions were selected as study areas, including Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia. The Municipalities are selected as the unit of analysis. The study's findings provide relevant information for defining targeted policies and balancing adaptation, mitigation, and development strategies. [ABSTRACT FROM AUTHOR]

The tendency for people to consider themselves morally good while behaving selfishly is known as moral hypocrisy. Influential work by Valdesolo and DeSteno (2007) found evidence for intergroup moral hypocrisy such that people were more forgiving of transgressions when they were committed by an in-group member than an out-group member. We conducted two experiments to examine moral hypocrisy and group membership in an online paradigm with Prolific workers from the United States: a direct replication of the original work with minimal groups (N = 610; nationally representative) and a conceptual replication with political groups (N = 606; 50% Democrats and 50% Republicans). Although the results did not replicate the original findings, we observed evidence of in-group favoritism in minimal groups and out-group derogation in political groups. The current research finds mixed evidence of intergroup moral hypocrisy and has implications for understanding the contextual dependencies of intergroup bias and partisanship. [ABSTRACT FROM AUTHOR]

When representing high-level stimuli, such as faces and animals, we tend to emphasize salient features—such as a face's prominent cheekbones or a bird's pointed beak. Such mental caricaturing leaves traces in memory, which exaggerates these distinctive qualities. How broadly does this phenomenon extend? Here, in six experiments (N = 700 adults), we explored how memory automatically caricatures basic units of visual processing—simple geometric shapes—even without task-related demands to do so. Participants saw a novel shape and then immediately adjusted a copy of that shape to match what they had seen. Surprisingly, participants reconstructed shapes in exaggerated form, amplifying curvature, enlarging salient parts, and so on. Follow-up experiments generalized this bias to new parameters, ruled out strategic responding, and amplified the effects in serial transmission. Thus, even the most basic stimuli we encounter are remembered as caricatures of themselves. [ABSTRACT FROM AUTHOR]

Confidence is an adaptive computation when environmental feedback is absent, yet there is little consensus regarding how perceptual confidence is computed in the brain. Difficulty arises because confidence correlates with other factors, such as accuracy, response time (RT), or evidence quality. We investigated whether neural signatures of evidence accumulation during a perceptual choice predict subjective confidence independently of these factors. Using motion stimuli, a central-parietal positive-going electroencephalogram component (CPP) behaves as an accumulating decision variable that predicts evidence quality, RT, accuracy, and confidence (Experiment 1, N = 25 adults). When we psychophysically varied confidence while holding accuracy constant (Experiment 2, N = 25 adults), the CPP still predicted confidence. Statistically controlling for RT, accuracy, and evidence quality (Experiment 3, N = 24 adults), the CPP still explained unique variance in confidence. The results indicate that a predecision neural signature of evidence accumulation, the CPP, encodes subjective perceptual confidence in decision-making independent of task performance. [ABSTRACT FROM AUTHOR]

According to accounts of neural reuse and embodied cognition, higher-level cognitive abilities recycle evolutionarily ancient mechanisms for perception and action. Here, building on these accounts, we investigate whether creativity builds on our capacity to forage in space ("creativity as strategic foraging"). We report systematic connections between specific forms of creative thinking—divergent and convergent—and corresponding strategies for searching in space. U.S. American adults completed two tasks designed to measure creativity. Before each creativity trial, participants completed an unrelated search of a city map. Between subjects, we manipulated the search pattern, with some participants seeking multiple, dispersed spatial locations and others repeatedly converging on the same location. Participants who searched divergently in space were better at divergent thinking but worse at convergent thinking; this pattern reversed for participants who had converged repeatedly on a single location. These results demonstrate a targeted link between foraging and creativity, thus advancing our understanding of the origins and mechanisms of high-level cognition. [ABSTRACT FROM AUTHOR]

It is commonly assumed that inner speech—the experience of thought as occurring in a natural language—is a human universal. Recent evidence, however, suggests that the experience of inner speech in adults varies from near constant to nonexistent. We propose a name for a lack of the experience of inner speech—anendophasia—and report four studies examining some of its behavioral consequences. We found that adults who reported low levels of inner speech (N = 46) had lower performance on a verbal working memory task and more difficulty performing rhyme judgments compared with adults who reported high levels of inner speech (N = 47). Task-switching performance—previously linked to endogenous verbal cueing—and categorical effects on perceptual judgments were unrelated to differences in inner speech. [ABSTRACT FROM AUTHOR]

Despite the intuitive feeling that our visual experience is coherent and comprehensive, the world is full of ambiguous and indeterminate information. Here we explore how the visual system might take advantage of ambient sounds to resolve this ambiguity. Young adults (n s = 20–30) were tasked with identifying an object slowly fading in through visual noise while a task-irrelevant sound played. We found that participants demanded more visual information when the auditory object was incongruent with the visual object compared to when it was not. Auditory scenes, which are only probabilistically related to specific objects, produced similar facilitation even for unheard objects (e.g., a bench). Notably, these effects traverse categorical and specific auditory and visual-processing domains as participants performed across-category and within-category visual tasks, underscoring cross-modal integration across multiple levels of perceptual processing. To summarize, our study reveals the importance of audiovisual interactions to support meaningful perceptual experiences in naturalistic settings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Erciyes Communication / Erciyes İletişim Dergisi is the property of Erciyes University, Faculty of Communication / Erciyes Universitesi Iletism Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arab International Journal of Knowledge Management is the property of Arab Institution of Knowledge Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets. [ABSTRACT FROM AUTHOR]

The Oum Er-Rbia watershed faces a critical challenge in effectively disseminating the results of groundwater and surface water quality assessments to stakeholders and the public. This research aimed to address this challenge by developing a method for synthesizing and visualizing knowledge through web mapping. The methodology followed involved collecting data on surface and groundwater quality, hydrogeology, human impact, and topography. Subsequently, a needs analysis was conducted to identify the requirements of potential users (land managers, water users, researchers) to define system functionalities. Spatial data was organized into thematic layers (e.g., hydrography, geology) with detailed attributes to address user queries and generate maps. Open-source technologies were employed to build a web-based system (SCIEM) allowing users to visualize, analyze, and explore water quality data. Finally, user evaluation confirmed SCIEM's effectiveness in disseminating and analyzing water quality information within the watershed. The developed web mapping system has successfully addressed the challenge of disseminating research results by establishing an online, interactive multi-scale mapping system for groundwater and surface water characterization. This system facilitates the presentation of maps with a simple click, making them accessible to both geospatial experts and non-specialists. As a result, stakeholders such as water managers, land-use planners, and even farmers and citizens can now access a comprehensive view of groundwater and surface water information. Additionally, the utilization of free and open-source software (IMSMS) has proven to be a cost-effective and efficient approach. The Interactive Multi-Scale Mapping System (IMSMS) provides a valuable tool for disseminating water quality data within the Oum Er-Rbia watershed. Building on this success, future research can explore the potential of adapting the IMSMS framework to other regions facing water quality challenges. Additionally, incorporating new data types beyond water management could provide a more holistic understanding of the interconnected factors influencing water resources.

While the phrase ‘public service’ commonly refers to public bodies based on legal principles, it can also be understood as the services rendered to the public. Therefore, fostering the relationship of trust between services and citizens – as the users of these public services – involves thinking very finely about attracting audiences and the interactions between users and politicians through public communication, regardless of the channel used. The low use of open data and information in public action translates into a poor level of trust towards public institutions in Romania, with no clear vision to urgently improve the situation. In order to change the architecture of the data of public interest, the central institutions must act towards the citizen’ environment choice and therefore, will improve the public trust in institutions.

Abstract Background Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper. Methods We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns. Results The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns. Conclusion Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

The purpose of the current study was to test the longitudinal association between disordered eating symptoms (body dissatisfaction, drive for thinness and bulimia) in adolescence (ages 12, 14, 16, 18, 19) and adulthood (age 40) in a sample of 883 white and Black women. We also investigated moderation by race. Adolescent symptoms at each time point significantly predicted adulthood symptoms for the body dissatisfaction and drive for thinness subscales, for both Black and white women. Bulimia symptoms in adolescence predicted symptoms in adulthood; however, the effect was largely driven by white women. Although moderation was non-significant, among white women, bulimia symptoms at all adolescent time points predicted adulthood bulimia, but among Black women, only symptoms at ages 18 and 19 were predictive of adulthood bulimia. Results suggest that both Black and white women are susceptible to disordered eating and that symptoms emerging in adolescence can potentially follow women into midlife.