Search

Much of what we know and love about music hinges on our ability to make successful predictions, which appears to be an intrinsically rewarding process. Yet the exact process by which learned predictions become pleasurable is unclear. Here we created novel melodies in an alternative scale different from any established musical culture to show how musical preference is generated de novo. Across nine studies (n = 1,185), adult participants learned to like more frequently presented items that adhered to this rapidly learned structure, suggesting that exposure and prediction errors both affected self-report liking ratings. Learning trajectories varied by music-reward sensitivity but were similar for U.S. and Chinese participants. Furthermore, functional MRI activity in auditory areas reflected prediction errors, whereas functional connectivity between auditory and medial prefrontal regions reflected both exposure and prediction errors. Collectively, results support predictive coding as a cognitive mechanism by which new musical sounds become rewarding. [ABSTRACT FROM AUTHOR]

Inattentional blindness is a phenomenon wherein people fail to perceive obvious stimuli within their vision, sometimes leading to dramatic consequences. Research on the effects of fear chemosignals suggests that they facilitate receivers' sensory acquisition. We aimed to examine the interplay between these phenomena, investigating whether exposure to fear chemosignals (vs. rest body odors) can reduce the inattentional-blindness handicap. Utilizing a virtual-reality aquarium, we asked participants to count how many morsels a school of fish consumed while two unexpected stimuli swam by. We predicted that participants exposed to fear chemosignals (N = 131) would detect unexpected stimuli significantly more often than participants exposed to rest body odors (N = 125). All participants were adult Portuguese university students aged 18 to 40 years. The results confirmed our hypothesis, χ2(1) = 6.10, p =.014, revealing that exposure to fear chemosignals significantly increased the detection of unexpected stimuli by about 10%. The implications of our findings open a novel avenue for reducing the adverse consequences of inattentional blindness. [ABSTRACT FROM AUTHOR]

Learning from data is a valuable skill for nonprofit professionals and researchers. Often, data have a spatial component, and data relevant to the nonprofit sector are no exception. Understanding spatial aspects of the nonprofit sector may provide immense value to social entrepreneurs, funders, and policy makers, by guiding programmatic decisions, facilitating resource allocation, and development policy. As a result, spatial thinking has become an essential component of critical thinking and decision making among nonprofit professionals. The goal of this case study is to support and encourage instruction of spatial data analysis and spatial thinking in nonprofit studies. The case study presents a local nonprofit data set, along with open data and code, to assist the instructors teaching spatial aspects of the nonprofit sector. Pedagogical approaches are discussed. [ABSTRACT FROM AUTHOR]

Background: Using personal data as Open Data is a pervasive topic globally, spanning various sectors and disciplines. Recent technological advancements, particularly in artificial intelligence and algorithm-driven analysis, have significantly expanded the capacity for the automated analysis of vast datasets. There's an expectation that Open Data analysis can drive innovation, enhance services, and streamline administrative processes. However, this necessitates a legally and ethically sound framework alongside intelligent technical tools to comprehensively analyze data for societal benefit. Methodology: A systematic review across seven databases (MEDLINE, CINAHL, BASE, LIVIVO, Web of Science, IEEExplore, and ACM) was conducted to assess the current research on barriers, support factors, and options for the anonymized processing of personal data as Open Data. Additionally, a supplementary search was performed in Google Scholar. A total of n = 1192 studies were identified, and n = 55 met the inclusion criteria through a multi-stage selection process for further analysis. Results: Fourteen potential supporting factors ( n = 14 ) and thirteen barriers ( n = 13 ) to the provision and anonymization of personal data were identified. These encompassed technical prerequisites as well as institutional, personnel, ethical, and legal considerations. These findings offer insights into existing obstacles and supportive structures within Open Data processes for effective implementation. [ABSTRACT FROM AUTHOR]

The open data movement has brought revolutionary changes to the field of mineralogy. With a growing number of datasets made available through community efforts, researchers are now able to explore new scientific topics such as mineral ecology, mineral evolution and new classification systems. The recent results have shown that the necessary open data coupled with data science skills and expertise in mineralogy will lead to impressive new scientific discoveries. Yet, feedback from researchers also reflects the needs for better FAIRness of open data, that is, findable, accessible, interoperable and reusable for both humans and machines. In this paper, we present our recent work on building the open data service of Mindat, one of the largest mineral databases in the world. In the past years, Mindat has supported numerous scientific studies but a machine interface for data access has never been established. Through the OpenMindat project we have achieved solid progress on two activities: (1) cleanse data and improve data quality, and (2) build a data sharing platform and establish a machine interface for data query and access. We hope OpenMindat will help address the increasing data needs from researchers in mineralogy for an internationally recognized authoritative database that is fully compliant with the FAIR guiding principles and helps accelerate scientific discoveries. [ABSTRACT FROM AUTHOR]

Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This 'academic openness quartet' is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied 'academic openness quartet' perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research. [ABSTRACT FROM AUTHOR]

Eurostat is charged with providing high-quality statistics for Europe, and the current information landscape is making it increasingly challenging to do so. This paper presents the Eurostat dissemination approach (including the traditional dissemination vectors), and thereafter proceeds to present the recent initiatives to make European statistics data and metadata available in the form of Linked Open Data (LOD). After presenting some of the main challenges for open data dissemination (complete reproducibility, availability of high-quality LOD, capacity to consume LOD and achieving meaningful mashups between official statistics LOD and other data sources), it concludes by noting the potential of LOD to foster transparency, reproducibility, collaboration, interdisciplinary research driving scientific advancements, and contributing to a broader understanding of complex scientific challenges. [ABSTRACT FROM AUTHOR]

Introduction: Perinatal mental health (PMH) conditions affect around one in four women, and may be even higher in women from some ethnic minority groups and those living in low socioeconomic circumstances. Poor PMH causes significant distress and can have lifelong adverse impacts for some children. In England, current prevalence rates are estimated using mental health data of the general population and do not take sociodemographic variance of geographical areas into account. Services cannot plan their capacity and ensure appropriate and timely support using these estimates. Our aim was to see if PMH prevalence rates could be identified using existing publicly available sources of routine health data. Methods: A review of data sources was completed by searching NHS Digital (now NHS England), Public Health England and other national PMH resources, performing keyword searches online, and research team knowledge of the field. The sources were screened for routine data that could be used to produce prevalence of PMH conditions by sociodemographic variation. Included sources were reviewed for their utility in accessibility, data relevance and technical specification relating to PMH and sociodemographic data items. Results: We found a PMH data 'blind spot' with significant inadequacies in the utility of all identified data sources, making it impossible to provide information on the prevalence of PMH in England and understand variation by sociodemographic differences. Conclusions: To enhance the utility of publicly available routine data to provide PMH prevalence rates requires improved mandatory PMH data capture in universal services, available publicly via one platform and including assessment outcomes and sociodemographic data. [ABSTRACT FROM AUTHOR]

Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous 'blue carbon' studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making. [ABSTRACT FROM AUTHOR]

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks' performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks' performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espiga is the property of Revista Espiga 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.)

Machine learning (ML) methods are commonly applied in the fields of extraterrestrial physics, space science, and plasma physics. In a prior publication, an ML classification technique, the Random Forest (RF) algorithm, was utilized to automatically identify and categorize erroneous signals, including instrument errors, noisy signals, outlier data points, and the impact of solar flares (SFs) on the ionosphere. This data communication includes the pre-processed dataset used in the aforementioned research, along with a workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of ML and ionospheric physics science, as well as being useful to other researchers for diverse objectives. Dataset: https://zenodo.org/records/10433194 (accessed on 26 December 2023) Dataset License: CC-BY 4.0 [ABSTRACT FROM AUTHOR]

Copyright of Dixi is the property of Universidad Cooperativa de Colombia 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.)

Viticulture and wine production have a long tradition and are a very important agricultural branch in Serbia with a potential that is still untapped. However, the development of viticulture had different periods and stages of development, from stagnation and war events, bad climatic and economic conditions to the placement and sale of wine on the domestic and world markets. Open data enables the management of tourist destinations, but in Serbia there are no or rare examples of good practice, studies and research on this matter. Smart tourist destinations make up the concept of tourist development, the basis of which lies in innovative solutions and modern technology. Numerous tourist destinations in Serbia is not even at the beginning of improving smart tourism in destinations or are just beginning to implement them. The Belgrade viticultural region has a high rating due to its proximity to the destination itself, primarily Belgrade, as the main and important emissions center, which makes the trip shorter, the longer stay and the participation of tourists more attractive. The capital of the Republic of Serbia is located in this region, as well as the immediate surroundings of other city centers with a large share of the population. Belgrade is the main transit point of Serbia: good traffic connections, as well as the proximity of the "Nikola Tesla" Airport. In the vineyards of Serbia, the formation of wine products and accompanying tourist content did not occur, but they remained in the tourist offer as part of the rural tourist offer. [ABSTRACT FROM AUTHOR]

In this paper we present a correlation based safe screening technique for building the complete Lasso path. Unlike many other Lasso screening approaches we do not consider prespecified values of the regularization parameter, but, instead, prune variables which cannot be the next best feature to be added to the model. Based on those results we present a modified homotopy algorithm for computing the regularization path. We demonstrate that, even though our algorithm provides the complete Lasso path, its performance is competitive with state of the art algorithms which, however, only provide solutions at a prespecified sample of regularization parameters. We also address problems of extremely high dimensionality, where the variables may not fit into main memory and are assumed to be stored on disk. A multidimensional index is used to quickly retrieve potentially relevant variables. We apply the approach to the important case when multiple models are built against a fixed set of variables, frequently encountered in statistical databases. We perform experiments using the complete Eurostat database as predictors and demonstrate that our approach allows for practical and efficient construction of Lasso models, which remain accurate and interpretable even when millions of highly correlated predictors are present. [ABSTRACT FROM AUTHOR]

This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management. [ABSTRACT FROM AUTHOR]

Copyright of Uporabna Informatika is the property of Slovensko Drustvo Informatika 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.)

Although open science has become a popular tool to combat the replication crisis, it is unclear whether the uptake of open science practices has been consistent across the field of psychology. In this study, we were particularly interested in whether claims that developmental psychology lags behind other subfields in adopting open science practices were valid. To test this, we determined whether data and material sharing differed as a function of psychological subfield at the distinguished journal, Psychological Science. The results showed that open data and open materials scores increased from 2014–2015 to 2019–2020. Of note, articles published in the field of developmental psychology generated lower open data and open materials scores than articles published in cognition; however, scores were similar to articles published in social psychology. Across Psychological Science articles, shared data and materials were seldom accompanied by documentation that is likely to make shared research objects useful. These findings are discussed in the context of the unique challenges faces by developmental psychologists and how journals can more effectively encourage authors to practice open science across psychology. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Arqueologia is the property of Revista de Arqueologia 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.)

IntroductionThe Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.MethodsWe used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.ResultsWe released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

IntroductionIn the scientific research domain, the Open Science movement stands as a fundamental pillar for advancing knowledge and innovation globally. This article presents the design and implementation of the GITCE Open Data Ecosystem (GITCE-ODE) Research Data Management System (RDMS), developed by the Research Group on Emerging Computational Technologies (GITCE) at the Technological University of Panama, as a platform for the long-term storage, publication, and dissemination of research products.MethodsThe architecture of the GITCE-ODE RDMS encompasses the entire data engineering lifecycle, facilitating information processing stages such as extraction, transformation, loading (ETL), as well as the management and analysis of diverse datasets and metadata.ResultsCompliance with the FAIR principles ensures that published data and products are Findable, Accessible, Interoperable, and Reusable, promoting automation in the discovery and reuse of digital resources. Key considerations of the web portal include file format standardization, data categorization, treatment of semantic context, and organization of resources to ensure efficient management and administration of open research data.DiscussionThrough this platform, GITCE aims to foster collaboration, transparency, and accessibility in scientific research, contributing to the ongoing advancement of knowledge transfer and innovation.

Open access to harmonised digital data describing Earth’s surface and subsurface holds immense value for society. This paper highlights the significance of open access to digital geoscience data ranging from the shallow topsoil or seabed to depths of 5 km. Such data play a pivotal role in facilitating endeavours such as renewable geoenergy solutions, resilient urban planning, supply of critical raw materials, assessment and protection of water resources, mitigation of floods and droughts, identification of suitable locations for carbon capture and storage, development of offshore wind farms, disaster risk reduction, and conservation of ecosystems and biodiversity. EuroGeoSurveys, the Geological Surveys of Europe, have worked diligently for over a decade to ensure open access to harmonised digital European geoscience data and knowledge through the European Geological Data Infrastructure (EGDI). EGDI acts as a data and information resource for providing wide-ranging geoscience data and research, as this paper demonstrates through selected research data and information on four vital natural resources: geoenergy, critical raw materials, water, and soils. Importantly, it incorporates near real-time remote and in-situ monitoring data, thus constituting an invaluable up-to-date database that facilitates informed decision-making, policy implementation, sustainable resource management, the green transition, achieving UN Sustainable Development Goals (SDGs), and the envisioned future of digital twins in Earth sciences. EGDI and its thematic map viewer are tailored, continuously enhanced, and developed in collaboration with all relevant researchers and stakeholders. Its primary objective is to address societal needs by providing data for sustainable, secure, and integrated management of surface and subsurface resources, effectively establishing a geological service for Europe. We argue that open access to surface and subsurface geoscience data is crucial for an efficient green transition to a net-zero society, enabling integrated and coherent surface and subsurface spatial planning.

In chemoinformatics, artificial intelligence (AI) continues to grow a symbiosis with open science (OS). Such a close AI-OS interaction brings substantial practical benefits in research, scientific dissemination, and education, to name a few areas. The AI-OS symbiosis can be further enhanced by combining sufficient substantive expertise, mathematical and statistical knowledge, and coding skills. This Viewpoint discusses the benefits of the smooth and productive interaction between AI, OS, and open data. We also present a short list of misconceptions and pitfalls surrounding AI-OS and propose correct responses and behaviors agreed upon by field experts. In addition, we provide suggestions to continue enhancing the positive contributions of the AI-OS symbiosis towards chemoinformatics.

The use of public transport data has evolved rapidly over the past decades. Indeed, the availability of diverse data sources and advances in analytics have led to a greater emphasis on utilizing data to enhance public transport services. Rail transit systems have increasingly become the preferred mode of travel due to their comfort, speed, and (mostly) emission-free nature. However, persistent delays continue to be a concern. Machine learning-based prediction of transit delays is an emerging field gaining recognition. The first contribution of this paper is to illustrate how to exploit available open data to improve the prediction of rail transit delays using machine learning. Moreover, through a comparison of various well-known machine learning approaches, we show that they can yield significantly different results. Notably, the improved support vector machine method presented in this study exhibits exceptional performance and is well-suited for long-term predictions. Furthermore, we have incorporated explainable artificial intelligence techniques to identify and assess the most significant factors influencing delays. To perform experiments with the method and draw robust conclusions, three case studies featuring different rail services in major cities are provided.

Abstract Premise Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications. Methods We built SDMs for the invasive aquatic plant European frog‐bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large‐scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence‐background or presence‐absence) of occurrence data. Results Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat. Discussion Our findings relative to European frog‐bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data‐centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

The issue of territorial data is of growing importance to the State, which aims to guide their production, circulation and conditions of use. This article examines how the State is repositioning itself vis-à-vis local authorities regarding urban planning regulations, in the context of the standardization and digitization of data from local urban development plans. It also explores the integration of these data into a single geo-platform. We demonstrate that this project to build a common tool on a national scale is part of a central government takeover of urban planning data through the partial integration of private methods developed by commercial companies during the 2010s, facilitated by the open data process. Studying the public/private production of regulatory urban planning data highlights two key aspects in the reconfiguration of State action regarding territories: on the one hand, the emergence of tension between a managerial logic of data and a territorially adapted approach to regulation, suited to the context of local projects, raises the question of a reassertion of the State in terms of urban planning regulations and a potential prevalence of "predefined boxes" over the nuanced and contextualized logic of the rule; on the other hand, the digitization and standardization of urban planning data establish the conditions for a technical instrumentation of territories to facilitate their monitoring and control through quantified territorial comparisons. This study concludes to a complexification of the contemporary production of territorial data while highlighting the importance of monitoring these data to grasp the issues embodied in new types of hybrid instruments for State steering of local action.

The underrepresentation of women in STEM fields needs to be highlighted through data to assist decision-makers and public policy creators in addressing the issue effectively. However, the lack of structured, organized data published openly in this domain is still a reality. To address this problem, a Latin American research network called ELLAS was created. The project's goal is to develop a platform with Semantic Web-based technologies to structure and concentrate data from Brazil, Peru, and Bolivia, initially. This paper presents the processes defined for the collection and curation of both unstructured and structured data, sourced from scientific articles, social networks, and existing open data. We explore the architecture design in a way that facilitates understanding of the details of the processes and the actors involved for each data source. We present the preliminary results from the application of these processes, and the strategies for future work, which include the data extraction and curation, and the ontology and knowledge graph development We also present some of the undergoing work, such as the survey development and application as well as showing what still hasn't been done, such as the platform development.

A broad understanding of the aims and objectives of the international open science movement was recently adopted with the 2021 UNESCO Recommendation on Open Science, expanding the focus of open science to include scientific knowledge, infrastructures, knowledge systems and the open engagement of societal actors. In response, recent discussions on science policy practice are shifting to the implementation of open science via national policies. While policy instruments to support some aspects of open science are well-studied, guidance on the emerging ‘social’ aspects of open science has lagged, prompting UNESCO to generate guidance. In this paper, several authors of the UNESCO Open Science Toolkit guidance document on ‘Engaging societal actors in Open Science’ synthesize the scholarly underpinnings behind its recommendations. This work draws upon a targeted search from academic, policy, and grey literature in the fields of open science and community engagement, with a special focus on citizen science, to derive guidance on how to overcome barriers to the uptake of societal engagement approaches. The results present building blocks of what an enabling environment for the open engagement of societal actors could look like, identifying key considerations and reflecting on opportunities and challenges for progressing and evaluating sound open engagement of societal actors into regional & national (open) science policies.

Objective: this research examines the collaborative development of a standard for publishing open data in public procurement and contracting, drawing on regional and international initiatives. The study posits that disseminating such a standard can potentially enhance the municipalities’ ability to disclose data for citizens and control agencies, complying with open data principles. Method: the study started with an examination of a specific issue encountered within a municipality, engaging a network of stakeholders possessing diverse perspectives and insights into the problem and potential solutions. A proposal to resolve the issue was formulated and subjected to a pilot test in a municipality, evaluating its capacity for interoperability and scalability to other municipalities and public agencies. Result: alongside the proposal for an open data standard comprising three classes of data — public notice, adjudication, and contracting —, the study yielded two additional outcomes: the development and systematization of a co-production methodology to address public issues, and the initiation of a project to establish a statewide network for open data standardization. Conclusion: the development of the open data standard involved the integration of diverse knowledge, resources, and stakeholders. In addition, it encountered challenges, underwent iterations, and imparted valuable lessons. The emphasis on problem-solving and collaborative engagement among municipalities, academia, civil society, and government agencies was an aspect that stood out. Constructed collaboratively, the open data standard demonstrated potential for replication and is recommended for adoption by other organizations seeking to promote open data initiatives. The technopolitical and ontological aspects of the process, along with the perspectives and challenges for its sustainability, contribute to advancing knowledge and enhancing the practice of collaborative governance.

Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008–2013 and 2015–2018. We asked authors if they would share data in a public repository—the Data Ark—or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67%–80%]) were not available and data for 7 (4%, 95% CI [0%–12%]) were available with restrictions. Data for 36 (22%, 95% CI [16%–30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.

The Social Survey of Andalusia is an instrument for monitoring the Andalusian reality developed by the regional government of Andalusia, whose dataset is open access to the citizens. The 2022 edition included questions related to happiness, social relations as well as socio-demographic factors. Based on this dataset, the present study aimed to analyse the relationship between socio-demographic factors and people’s experiences of happiness. It also set out to explore which factors might be indispensable for experiencing moments of happiness as measured in the survey. A sample of 4,968 cases was gotten, conducting a descriptive analysis, a logit regression in complex samples, and a Necessary Conditions Analysis. The results found two very different social profiles in terms of the experience of happiness, determined by age, sex, educational level and economic status. However, neither factor proved to be a necessary condition for happiness. Both conclusions should be taken into consideration in any socio-community intervention.

Background Following a flurry of policies for Open Science (OS), there is now a wave of initiatives to monitor its adoption. However, the great diversity of understandings and activities related to Open Science makes monitoring very challenging. There is a danger that by focusing on what can be readily observed (e.g. publications) many other OS activities are overlooked (e.g. participation), with a potential narrowing of OS scope, streetlight effects, and deviation from the values of OS. Since Open Science can be understood as a systemic transformation of the research system, we have borrowed concepts from Transformative Innovation Policies frameworks which aim at evaluating socio-technical transitions. In accordance with this view of OS as a systemic transformation, we propose that the new monitoring efforts should shift towards: (i) systemic perspectives which considers the various actions related to OS, including policies and outputs (e.g. datasets) but also processes (e.g. participatory events), outcomes (e.g. citizen interest in science) and expected impacts (e.g. better scientific contributions to addressing societal problems); (ii) implementation of monitoring as reflexive learning (rather than accountability or benchmarking); (iii) mapping the directionality of the activities and the values associated with the choices in directions. In summary, a monitoring framework for OS requires a profound change in conventional monitoring practices. The scope should broaden from current focus on outputs (such as publications) towards the processes of connection that make science ‘open’ (usage, co-creation and dialogue), as well as towards outcomes (changes in practices) and the longer-term impacts that reflect the values and normative commitments of OS.

The purpose of the article is to analyse and present the use of Open Science principles and their implementation in the activities of Lutsk National Technical University. Methodology. The main provisions of the Open Science concept and their implementation in European and Ukrainian official documents are analysed. It has been found that at Lutsk National Technical University (LNTU) the principles of Open Science are implemented in three main areas: in the educational process; in research activities; in informal promotion among students and other stakeholders. As a result, the authors found that participation in international projects, in particular ERASMUS+, causes changes in educational and scientific activities towards the creation of a fair and competitive educational space. New disciplines, such as Open Scientific Foundations and Information Management Practice, have been introduced into the educational process of masters and postgraduate students. The implementation of the policy of openness of scientific research is mainly limited to open publications in scientific journals and public discussion of research results at conferences and seminars. Informal trainings on academic integrity and Open Science involving students and teachers help to promote the principles of Open Science. The effectiveness of the training for LNTU students has been experimentally established. After the training, most students understood the basic principles of Open Science and were able to give examples of their use at LNTU, but they are not yet ready to apply them in their first scientific or academic achievements. Practical implications. These examples of implementation of Open Science principles in educational and scientific activities of LNTU illustrate an important vector of development of the technical university towards joining the European higher education area. At the same time, the authors came to the conclusion that it is necessary to conduct a broad information campaign among research and teaching staff for their further implementation.

Against increasing injunctions in research governance to create open data, and knee-jerk rejections from qualitative researchers in response to such efforts, we explore a radical counter movement of academics engaged in what we term “DIY Academic Archiving,” the creation of open and accessible archives of their research materials. We turn to interviews with three DIY academic archivists, each drawing on an ethos of community archiving, as opposed to emerging open data schemes: Melissa Munn on The Gaucher/Munn Penal Press Collection,1 Eric Gonzaba’s Wearing Gay History,2 and Michael Goodman’s Victorian Illustrated Shakespeare Archive.3 We see these archives as engaged in a “politics of refusal,” which challenges both conventional methods and ethics in qualitative research as well as new moves toward open data. On the one hand, academics are tasked to “protect” their data by destroying it, under the guise of a supposed mode of “care.” On the other hand, open data makes quite contrary demands, to care for data by making it “open” for further extraction through (re)use. DIY Academic Archiving is a practice of refusal that supports a redirection away from this binary. In this article, we explore how DIY academic archivists play with coding as a form of mischievous disruption, and so are contributing to new data imaginaries. We offer insight into how DIY Academic Archiving supports researchers in their theoretical, methodological and political commitments, and at the same time, how it can enable researchers to take the care-full risk of archiving our research data.

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

Resisting immediate temptations in favor of larger later rewards predicts academic success, socioemotional competence, and health. These links with delaying gratification appear from early childhood and have been explained by cognitive and social factors that help override tendencies toward immediate gratification. However, some tendencies may actually promote delaying gratification. We assessed children's delaying gratification for different rewards across two cultures that differ in customs around waiting. Consistent with our preregistered prediction, results showed that children in Japan (n = 80) delayed gratification longer for food than for gifts, whereas children in the United States (n = 58) delayed longer for gifts than for food. This interaction may reflect cultural differences: Waiting to eat is emphasized more in Japan than in the United States, whereas waiting to open gifts is emphasized more in the United States than in Japan. These findings suggest that culturally specific habits support delaying gratification, providing a new way to understand why individuals delay gratification and why this behavior predicts life success.

The article discusses the importance of balancing open access to forest data with the need to protect the integrity of National Forest Inventory (NFI)-derived information. The authors argue that revealing the exact locations of inventory plots could compromise the randomness and representativeness of the data. They suggest that scientific data use should be considered legitimate and propose a linkage between access to geolocated data and a nondisclosure agreement to ensure data and plot integrity. The authors also mention alternative solutions, such as using high-performance computing systems or uploading NFI data into Earth observation agencies' platforms. They emphasize the importance of data sharing for scientific purposes and call for collaboration and open discussion between data providers and users to achieve a balance between different needs. The authors acknowledge the different situation in the Global South, where fair collaborations and funding transfers are necessary. They conclude that full data availability for science, combined with nondisclosure agreements and alternative procedures, is the way towards open science, good scientific practice, and unbiased results. [Extracted from the article]

The aim of the article is to assess the level of digitalization of the economy of Ukraine and the EU countries using international indices and rankings. In international practice, various rankings and indices are increasingly used to assess the progress of countries around the world in the field of digitalization. The most common rankings that characterize the level of digitalization of the national economy include: the Digital Economy and Society Index (DESI); the ICT Development Index; the E-government development index; the Global Cybersecurity Index; the ranking of countries by the level of open data development (Open Data Maturity), etc. The presented international indices summarize the relevant indicators that characterize the effectiveness of digital technologies in the countries of the world; accessibility and quality of mobile communications, the Internet, and digital public services; protection from cyber threats; effectiveness of State policy in the field of open data. The features of formation and constituent components of international rankings and indices, used to assess the level of digitalization of the economy, are considered. The EU countries that are leaders in the digitalization of the economy include: Denmark, Estonia, Poland, Finland, France. The competitive disadvantages of Ukraine in comparison with the EU countries in terms of the components of international indices are determined. The vectors of digital development of Ukraine have been formed as follows: ensuring high-quality communication by expanding access to broadband Internet and its quality; improvement of telecommunications infrastructure; security against cyber threats; strengthening the effectiveness of the State digital policy. It is concluded that in Ukraine there is a potential for increasing the partial indicators that form integral indices, which are used in international practice to assess the level of digitalization of the economy.

Ahmed S BaHammam1– 3 1Editor-in-Chief Nature and Science of Sleep; 2Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, Riyadh, Saudi Arabia; 3King Saud University Medical City, Riyadh, Saudi ArabiaCorrespondence: Ahmed S BaHammam, Professor of Medicine, University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Box 225503, Riyadh, 11324, Saudi Arabia, Tel +966-11-467-9495, Fax +966-11-467-9179, Email ashammam2@gmail.com

Here, we introduce SPARSE (acronym for "SPecies AcRoss ScalEs"), a simple and portable template for databases that can store data on species composition derived from ecological inventories, surveys and checklists, with emphasis on metadata describing sampling effort and methods. SPARSE can accommodate resurveys and time series and data from different spatial scales, as well as complex sampling designs. SPARSE focuses on inventories that report multiple species for a given site, together with sampling methods and effort, which can be used in statistical models of true probability of occurrence of species. SPARSE is spatially explicit and can accommodate nested spatial structures from multiple spatial scales, including sampling designs where multiple sites within a larger area have been surveyed and the larger area can again be nested in an even larger region. Each site in SPARSE is represented either by a point, line (for transects) or polygon, stored in an ESRI shapefile. SPARSE implements a new combination of our own field definitions with Darwin Core biodiversity data standard and its Humboldt core extension. The use of Humboldt core also makes SPARSE suitable for biodiversity data with temporal replication.We provide an example use of the SPARSE framework by digitising data on birds from the Czech Republic, from 348 sites and 524 sampling events, with 15,969 unique species-per-event observations of presence, abundance or population density. To facilitate use without the need for a high-level database expertise, the Czech bird example is implemented as MS Access .accdb file, but can be ported to other database engines. The example of Czech birds complements other bird datasets from the Czech Republic, specifically the four gridded national atlases and the breeding bird survey which cover a similar temporal extent, but different locations and spatial scales.

Open Science paradigm involves transformations throughout the entire cycle of scientific research. Decision-makers play the key role of paradigm change facilitators. Consequently, the promotion of Open Science requires a political commitment.

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively.

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.