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Schooling in Australia has become subject to increased processes of data-based governance. This article draws upon the insights of an experienced teacher, 'Meriam', who, having taught more than 34-years over almost a 50-year span, reflected upon the nature of such changes. Utilising theorising in relation to datafication processes and accountability logics, the article elaborates Meriam's efforts to engage productively with increased attention to measuring and monitoring students' performance via various modes of data. This is in the context of increased pressure in the state of Queensland to justify teachers' practices, and to show how their students' learning had shown improvement on these data over short periods of time. In this sense, the article challenges a more post-performative conception of professionalism as Meriam struggled to comprehend how the increased attention to a plethora of data could serve as a useful vehicle to inform teachers' work and learning; this was the case even as she found some benefits in increased attention to her own practice and learning. The article delineates the constraints associated with increased attention to data, and how current managerial and neoliberal policy conditions in schools may contribute to potentially significant and harmful effects for both teacher and student learning. [ABSTRACT FROM AUTHOR]

Graduates of Master of Public Health programs may lack appropriate skills in data analysis, and would benefit from practice with research data. Datasets that contain health information relevant to student interests, and that are appropriate sizes for class use can be difficult to locate. The National Health and Nutrition Examination Survey is a study that collects both survey and health examination information from a national sample every 2 years. I present a sample of this dataset, with examples of how to use it for human health related questions. Instructions for how to access and create additional, customized datasets are also provided. Instructors may consider investigating this rich data source and providing students with subsets of these data for class assignments, projects and master's theses. [ABSTRACT FROM AUTHOR]

Existing research suggests that alternative data are mainly informative about short‐term future outcomes. We show theoretically that the availability of short‐term‐oriented data can induce forecasters to optimally shift their attention from the long term to the short term because it reduces the cost of obtaining short‐term information. Consequently, the informativeness of their long‐term forecasts decreases, even though the informativeness of their short‐term forecasts increases. We test and confirm this prediction by considering how the informativeness of equity analysts' forecasts at various horizons varies over the long run and with their exposure to social media data. [ABSTRACT FROM AUTHOR]

The digital transformation of architecture, engineering, and construction (AEC) is being hailed as a panacea for the sectors' problems, with major actors in government and business promoting it as a benevolent harbinger of greater efficiency, productivity, better quality, and environmental performance. However, advancing digitalization in AEC is intertwined with a distinct political-economic logic that involves new constraints, dependencies, and a reconfiguration of power relations. Specifically, Building Information Modeling (BIM) as a digital convergence of three-dimensional building models, databases, and interfaces for multidisciplinary collaboration operates as a runway technology for digitalization and an entry point for platformization impelled by global software giants, such as this article's empirical case of software company Autodesk and its BIM product portfolio shows. BIM's linkage of governmental policy strategies with the techno-economic platform logic of software subscription models, open application programming interfaces, and cloud-enabled collaboration ensures management and control of construction data and software interoperability. At the same time, BIM's techno-economic and political configuration facilitates a near-monopolistic structure in the global architecture and construction software market. It thus grants one hegemonic company immense power to define what formats, standards, and data for building models and design processes are being used in creating the built environment. The expansive, intersecting logics of assetization, platformization, and datafication have been taking hold in the AEC sector, raising questions about their stratifying effects in the built environment and society. [ABSTRACT FROM AUTHOR]

Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through storytelling about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through standardization, staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through formatting, data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through cultivation, staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes—which silence unusual behaviours in the data—mean for the knowledge produced in data-intensive research. [ABSTRACT FROM AUTHOR]

Research on food system framework development has increased substantially in recent years to better understand how we can best transform food systems. However, these frameworks generally take a one-size-fits-all approach, with food system indicators lacking relevance in many countries and territories. This research, centred on the Solomon Islands as a case study, employed a participatory approach involving stakeholders to identify context-specific indicators across three food systems pathways. These pathways, reflecting context-specific goals and a collective vision, encompassed strengthening and connecting rural food systems, enhancing the national policy environment, and advocating for food environments supporting healthy food accessibility, affordability, and convenience. The research identified a range of contextspecific indicators and evaluated their alignment with existing data sources. Results underscored the need for data collection and analysis efforts to inform evidence-based decision-making. The study also emphasised the importance of engaging diverse stakeholders to ensure a comprehensive and representative set of indicators, aligning with regional and global efforts to advance food system resilience and sustainability. This research lays the foundation for future endeavours to enhance food system monitoring and assessment, acknowledging nuances and complexities specific to the Solomon Islands and similar contexts. [ABSTRACT FROM AUTHOR]

Copyright of International Sociology is the property of Sage Publications Inc. 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.)

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback–Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines. [ABSTRACT FROM AUTHOR]

Decades into the digital era, publicly available data on Black, indigenous, and people of color (BIPOC) owned businesses are still remarkably lacking. In this commentary piece, the authors cover recent literature on the importance of BIPOC-owned businesses for equitable growth and BIPOC wealth-building. The authors then examine the sources of data that are available – public and private – for tracking the success or failure of such businesses. A BIPOC business database was constructed from a variety of sources and used by the authors to examine the potential pitfalls of such exercises. The authors close with a call for better data collection on BIPOC businesses. [ABSTRACT FROM AUTHOR]

The need for information about weather conditions in a particular environment is very necessary for the community, so that they can plan activities for the future. A forecaster cannot process data to predict weather or climate conditions if it is not accompanied by historical data within a predetermined time period. Data is a very important savings for future generations, because data will make the nation's generation smart. Without data, all previous information would not be known to be studied. The current condition of the earth is very worrying and not easy to predict. Therefore, it is very important to have integrated observations and services in coastal and marine areas to support resilience to climate change and other marine hazards.Sustainable marine observations and services are very important and relevant to reduce potential problems and threats arising from climate change.In this paper we will discuss the creation of a tool for monitoring weather, where this tool will take data on humidity, temperature and rainfall. so that from this data we can predict weather conditions in the area. By using the fuzzy method, the combination of these three data can be used by fishermen to make decisions about whether it is still safe to go to sea.The system is built based on Arduino using muoring buoy media. where this tool will be placed in the sea. The power supply system uses solar cells, so that it can independently meet the power needs of the equipment. From the test results the system can work normally and well.

Social simulation studies are complex. They typically combine various data sources and hypotheses about the system’s mechanisms that are integrated by intertwined processes of model building, simulation experiment execution and analysis. Various documentation approaches exist to increase the transparency and traceability of complex social simulation studies. Provenance standards enable the formalization of information on sources and activities, which contribute to the generation of an entity, in a queryable and computationally accessible manner. Provenance patterns can be defined as constraints on the relationships between specific types of activities and entities of a simulation study. In this paper, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analysing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of an agent-based simulation project and by presenting its implementation in a web-based tool.

Energy systems modelling plays a pivotal role in understanding and optimizing complex energy systems. By integrating various factors such as energy demand, supply, infrastructure, and environmental considerations, energy systems modelling provides valuable insights for policymakers, industry stakeholders, and researchers. This can be key to informing stakeholder and policy decisions and facilitate the mobilisation of capital and market development to support the development of the energy sector. This article presents the data, assumptions, and related calculations used for the development of a national scale power system model for Sierra Leone. The focus of the model was a techno-economic analysis of specific hydropower expansion plans. Where possible, this data has been collected from publicly available sources such as scientific literature, feasibility studies, international databases, data from existing modelling efforts in Sierra Leone, and local stakeholders. The collection of the data, development of the model, and creation of scenarios was done in collaboration with the Ministry of Energy in Sierra Leone and verified by a broad range of stakeholders.The following paper outlines the key data presented in the full database which can be accessed through the link found in the Specifications Table. The link also contains the Reference Energy System (RES) used in this study.

This paper presents a comprehensive dataset on the global trade dynamics of COVID-19-related medical products for the years 2019 and 2020. The dataset, derived from the BACI database, focuses on eight distinct product categories identified by six-digit codes. The trade flow data for 224 countries is structured as a multilevel network, with countries as nodes and product categories as layers. Directed edges represent trading activities, and edge weights are determined by the difference in exported values between 2019 and 2020. The dataset is provided in an edges-and-nodes format. Additionally, the associated R script transforms the data into the MuxViz R package format, facilitating further analysis and visualization of the dataset. The dataset is valuable for researchers in the field of foreign trade or medical products, and for decision-makers in these fields, whether at company or national level.

The study of platforms, or multi-sided markets, has seen exponential growth in economics, media studies, social sciences and humanities. However, the critical political economy of media has been relatively quiet, with notable exceptions in areas such as platform capitalism, imperialism and especially platform labour and gig work. However, traditional Marxian production, often taken as the starting point, relies on the labour theory of value and struggles to capture all platform specificities. Due to their integrated understanding of production and circulation of value, I will argue that New Readings of Marx and Open Marxism help study platforms as linchpins of internet user lifeworlds and the global economy. In this literature, capitalism is analysed as a historically specific social form of production. Additionally, these approaches can address some theoretical problems that occasionally occur with related Marxian theories, such as unpaid digital labour, rent, and techno-feudalism. In that sense, this paper aims to contribute to the critical political economy of the media and the study of platforms by bringing previously neglected theoretical approaches to the centre stage and unpacking platforms as social forms of production and circulation in contemporary capitalism.

The original vision on what later became LifeWatch ERIC started about a quarter of a century ago in 1996. In those days, the promise of digital technologies entered biodiversity and ecosystem research. Not only by digitiing relevant information, but also with applications to process such data. While several (inter)national initiatives embarked on specific topics, there was also an idea that the upcoming view on grid computing provided attractive solutions for federated data sources, together with a strong computing capacity. This paper presents the history from conception to early actions, until actual preparations towards a research infrastructure on the European scale.

Irrigation efficiency projects aim to conserve water for in-stream flow and agricultural use by reducing water losses throughout the system. Piping irrigation canals is a common irrigation efficiency method that results in trade-offs: while it increases efficiency of irrigation water conveyance, it reduces incidental groundwater recharge. This paper focuses on the data and decision-making of canal piping, focusing primarily on understanding the potential impacts of reduced canal leakage on shallow wells. By conducting a spatial analysis of shallow wells in the basin at risk of being impacted by canal piping, and combining this with interviews with water managers in central Oregon's Upper Deschutes Basin, we demonstrate the complex socio-natural dynamics and politics of water conservation decision-making. The research finds that irrigation canal piping is fully supported by water managers in the study area as a means of physically shifting flows of water towards particular valued uses, yet at the same time there is not enough data to understand the potential impacts of canal piping on water users reliant on canal seepage. Given the lack of localized shallow groundwater monitoring data, water managers are reliant upon basin-scale model predictions when defining the trade-offs in canal piping. Broadly, the research demonstrates that well-intended water management decisions can have trade-offs and impacts that are not well understood, pointing to the need for more groundwater monitoring and critical attention to the multiple scales of irrigation efficiency to inform the co-management of surface water and groundwater for the many water users within a basin. • Irrigation efficiency can impact basins in ways that are not understood. • Water managers rely on incomplete evidence when making efficiency decisions. • Irrigation efficiency efforts may negatively impact shallow domestic wells. • The politics of data gaps must be taken seriously in water management. • Critical lenses help to understand the complexity of irrigation efficiency. [ABSTRACT FROM AUTHOR]

Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated "COVID-19 Vaccines Solutions Dashboard" in 2021-2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable. [ABSTRACT FROM AUTHOR]

Human trafficking represents the second most profitable criminal activity in the world. Here, based on the snowball sampling method, we obtained a novel dataset of a human trafficking network on the southern border of Mexico. This dataset was used to construct an unweighted and undirected graph that represents the interactions of the trafficking network. Our analysis reveals a moderate level of centralization at 44.32% and a medium density of 0.401, indicative of a structural balance that facilitates the coordination of criminal activities without a single actor's dominance. Addressing the challenge posed by the network's minimal cohesiveness, which hampers the sharing of resources among members, we assess four dismantling strategies: random removal, targeting hubs and brokers, a human capital-focused approach, and Generalized Network Dismantling (GND). Our findings underscore the efficacy of targeting moderately connected actors, a strategy that disrupts the network's resilience and operational capacity by severing important but inconspicuous connections, thereby destabilizing the network's efficiency subtly and avoiding immediate alert to the dismantling activities. This work is a significant contribution to the field of criminal network modeling and analysis. [ABSTRACT FROM AUTHOR]

Data literacy, which is considered among 21st century skills, is becoming increasingly important. In particular, incentives for data-driven decision-making draw attention to data literacy. Data literacy is also critical for teachers who need to use data in educational settings. Therefore, there is a need for data on whether teachers are equipped with data literacy competencies before their service. In this study, pre-service teachers' data literacy competencies were examined. The case study approach was used in the study, which was conducted as qualitative research. The study group consisted of 61 pre-service teachers studying in three different undergraduate programs. Data were collected through the "What do the data say?" activity instrument. Rubrics were used to analyze the data. The findings of the study showed that while there was no difference in terms of using data and data communication, there was a statistically significant difference between the programs in terms of data recognition, comparing data and establishing relationships between data competencies, as well as total data literacy. In addition, it was found that the majority of pre-service teachers were partially inadequate or inadequate in terms of using data, data communication and total data literacy. Nearly half of them were partially inadequate or inadequate in comparing data and establishing relationships between data. The results indicated that preservice teachers have certain deficiencies regarding data literacy. [ABSTRACT FROM AUTHOR]

The text diagnoses two opposing tendencies in the research on algorithms: the first abstracts and unites heterogeneous developments under the term "algorithm"; the second emphasizes specifics such as data sets, material conditions, software libraries, interfaces, and so on, thus dissolving that which apparently algorithms do into more fine-grained analyses. The text proposes a research perspective that resolves this tension by conceiving of algorithms as a relation between the abstract and the concrete that allows to capture both in their interdependence. This approach is informed by two motives: first, the necessity to connect detailed analyses of specific information technologies with general political concerns; and second, the application of recent feminist critiques of epistemology to the analysis of algorithms. The ensuing relational perspective on algorithms is connected to the genealogy of algorithmic technology before being demonstrated regarding the mutually complementing relationships: algorithms-materiality, algorithms-data, algorithms-code, and algorithms-interfaces. [ABSTRACT FROM AUTHOR]

Research Summary: We evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial prediction gains, suggesting that even simple data may be helpful. However, these gains did not result in improved decisions. Inspectors often used their decision authority to override algorithmic recommendations, partly to consider other organizational objectives without improving outcomes. Interviews with 55 departments find that while some ran pilots seeking to prioritize inspections using data, all provided considerable decision authority to inspectors. These findings suggest that for algorithms to improve managerial decisions, organizations must consider both the returns to algorithms in the context and how decision authority is managed. Managerial Summary: We evaluate a pilot in an Inspections Department to explore the returns to algorithms on decisions. We find that the greatest gains in this context come from integrating data into the decision process in the form of simple heuristics, rather than from increasing algorithmic sophistication or additional data. We also find that these improvements in prediction do not fully translate into improved decisions. Decision‐makers were less likely to follow data‐driven recommendations, partly in consideration of other organizational objectives, but without substantially improving on them overall. These findings suggest that organizations should consider the returns to technical sophistication in each context, and that the design and management of decision authority can be a key choice that impacts the value organizations can capture from using predictive analytics. [ABSTRACT FROM AUTHOR]

Copyright of Sociologia Italiana is the property of EGEA S.p.A 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.)

Using data from seven R1 and R2 universities in Ohio, this study seeks to determine the extent of usage of full-text e-journal articles stored across multiple platforms. Specifically, COUNTER 5 data is presented from five major academic journal publishers (Taylor & Francis, SAGE, Oxford, Wiley, and Springer), looking at usage within each of the publishers' respective platforms, as well as in EBSCO, JSTOR, and OhioLINK's Electronic Journal Center platform. The overarching goal of the analysis is to identify where exactly usage occurred for this e-journal content within the different platforms, especially in cases where an individual title has duplicate full-text access. Analysis of this data was conducted using Tableau and Excel. Findings raise significant questions about end-user behavior in terms of students' and faculty's ability to locate full text, regardless of whether it is discoverable from Google or other commercial search engines. The findings also suggest a strong connection between EBSCO usage and the academic calendar, with peak usage (as a percentage of overall usage) occurring in October, November, March, and April. Lastly, findings for Taylor & Francis e-journals within EBSCO call into question what these duplicate holdings mean for "big deal" subscription packages. [ABSTRACT FROM AUTHOR]

Active equity managers can improve investment performance by identifying new data sources that predict stock returns. These new sources are known as alternative data and comprise an emerging financial data ecosystem. Integrating these value-added data sources into existing investment decision processes is challenging, and the value of these novel sources declines rapidly as competing managers adopt the same data. This study presents an innovative data strategy that simplifies the data adoption process and sustains the competitive advantage of the data. The proposed data strategy crowdsources stock-picking ideas via the Vestly stock trading game app, which drives the data-generation process (DGP) and collects ideas from players. The authors show that the Vestly DGP can deliver profitable investment signals. A simple long–short strategy generates significantly positive stock-specific returns over four years with an information ratio of 0.72. The authors' study demonstrates that managers can create their alternative DGP as part of a strategy to remain competitive in the new era of alternative data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Higher Education / Yüksekögretim Dergisi is the property of Deomed Publishing 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.)