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An editorial introduction to the September 2015 issue is presented, highlighting its central theme of educational technology research data papers and asserting the importance of sharing various data sets from experimental studies between educators for the benefit of the discipline.

Data papers, such as those published by Ecological Research, encourage the retrieval and archiving of valuable unpublished, undigitized ecological observational data. However, scientists remain hesitant to submit their data to such forums. In this perspective paper, we describe lessons learned from the Long‐Term Ecological Research, the Global Biodiversity Information Facility and marine biological databases and discuss how data sharing and publication are both powerful and important for ecological research. Our aim is to encourage readers to submit their unpublished, undigitized ecological observational data then the data may be archived, published and used by other researchers to advance knowledge in the field of ecology. Coupling data sharing and syntheses with the development of innovative informatics would allow ecology to enter the realm of big science and provide seeds for a new and robust agenda of future ecological studies. [ABSTRACT FROM AUTHOR]

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink. [ABSTRACT FROM AUTHOR]

Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms such as data deluge and big data today characterize numerous (research) initiatives, much work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, and quality issues to a rather general lack of a data-sharing culture. Lately, data journals have overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for data set description, availability, citation, quality, and open access. We close by identifying ways to expand and strengthen the data journals approach as a means to promote data set access and exploitation. [ABSTRACT FROM AUTHOR]

Copyright of Policy & Internet is the property of Wiley-Blackwell 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.)

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post‐operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most‐widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions. This paper reviews the latest developments of supervised machine learning (ML) techniques that have been used to analyse data related to post‐operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions. [ABSTRACT FROM AUTHOR]

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists. [ABSTRACT FROM AUTHOR]

In this paper I argue through the double entendre of ‘data‐bility’ that how dateable one is on a dating app relies on data. This techno‐social framework enables an understanding of how dating apps are reconfiguring a politics of sexuality, circumscribed by digital technologies and data. Drawing on research with middle‐class women and gender‐minority dating app users in Mumbai and one dating app executive, the paper investigates how algorithms and users' digital behaviour together constitute data‐bility in three ways. First, dating app algorithms are designed to match those of similar social identities to one another. Second, dating app users engage with others' digital data on profiles and through message chats, reading class through these processes, deciding who to match/reject and correspondingly who is data‐ble. Third, users and algorithmic infrastructures come together to create new regimes of verification, through deeming some users ‘real’ and others ‘fake’ on dating apps, extending violent legacies of categorisation. Together, these processes result in data‐bility, a techno‐social order of digital dating oriented around the exclusion of those labelled ‘creeps’ along class and caste lines. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims at being a comprehensive reference for stakeholders, policy makers, and scholars interested in analyzing the problem of efficiency, effectiveness, and impacts of rail transport systems in a sound empirical way, paying specific attention to passenger transport services. The paper combines different analytical frameworks (engineering, economics, impacts), systematic review techniques, and advanced mappings. Framing economic efficiency studies into a transport planning perspective permits to move from efficiency to effectiveness issues. In addition, including impacts offers a critical discussion of the existing empirical studies, relating them to the main methodological approaches used. This analysis can be useful for those interested in developing new techniques for the evaluation of this sector. The critical analysis developed in this paper provides a catalog of inputs, outputs, external factors, possible impacts to account for, data, and approaches, which allows us to identify areas in which new methodological developments, new approaches, are needed to address the relevant societal challenges of the rail transport sector. [ABSTRACT FROM AUTHOR]

Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi‐structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case‐by‐case analysis, as each instance will likely produce a different outcome. [ABSTRACT FROM AUTHOR]

Tracking farmland purchases is central to interpreting transnational finance's growing power in agrarian restructuring. Australia's public Register of foreign land ownership reveals little about agrarian change, however. In presenting the first comprehensive mapping of farmland purchases made between 2008 and 2020, this paper examines the ways that financial investments are altering farm ownership patterns in Australia. First, we show that most foreign owned land has been purchased by only 10 pastoral companies, which are implicated in speculative development activities. Second, foreign investment in cropping and horticulture is more significant than it appears in the Register, with investments in agricultural infrastructure increasingly driving land use change. Third, we illustrate the deepening entrenchment of institutional finance. By engaging with the findings from our dataset as well as with the politics of data that have shaped the availability of information, the paper progresses understandings of the financialization of farmland in both its material and ideational aspects. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management is a crucial issue in socioeconomic metabolism and requires multicategory and high‐resolution data, and data on the physical composition of municipal solid waste (PCMSW) are fundamental in MSW research. Extensive financial resources have been invested in the research on field investigations of PCMSW; however, it is time‐consuming and sometimes not truly representative of the studied case. In this work, two bottom‐up and two top‐down approaches were applied for analyzing the PCMSW, namely, field investigation (FI), BP neural network (BPNN), material flow analysis (MFA), and inversion algorithm based on electricity generation of waste incinerator (IAEI). Wuhan City, China, was chosen as the studied case for analyzing and comparing the PCMSW results. The PCMSW values obtained by applying those four methods showed acceptable differences, and the standard deviations of organic fraction, ash and stone, paper, plastic and rubber, textile, wood, metal, glass, and others were 3.94%, 2.77%, 6.57%, 2.22%, 2.49%, 1.36%, 0.53%, 1.19%, and 0.28%, respectively. Use of the MFA, BPNN, and IAEI methods could reduce time and labor spent on manual sampling, sorting, and weighing of MSW, compared to the FI method. BPNN algorithm advances in providing PCMSW data in history and by trajectory throughout the year, whereas IAEI contributes PCMSW data with much higher temporal resolution. Data quality and applicability of different methods were discussed, considering the availability of time, labor, and reference data. [ABSTRACT FROM AUTHOR]

Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty‐two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data‐sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society. We tested studies published in the Journal of Wildlife Management and the Wildlife Society Bulletin for computational reproducibility. We found that the authors who were willing to share their data were largely using reproducible techniques but that our overall difficulty in obtaining data necessary to review reproducibility indicates that we can improve the scientific process within the wildlife sciences. [ABSTRACT FROM AUTHOR]

The article offers information on several papers being requested for the International Association for Research in Income and Wealth conference to be held in St. Gallen, Switzerland from August 22-28, 2010 which includes the topics Innovation--Key Factor for Economic Growth, House Price Movements and the Economy, and Surveys and Data Combination.

Floods are one of the most devastating natural disasters that can cause large economic damage and endanger human lives. Flood forecasting is one of the flood risk mitigation measures serving to protect human lives and social estate. The Danube River Basin (DRB) is the world's most international river basin, flowing through the territory of 19 countries, covering more than 800,000 km2. The frequency of floods in the DRB increased in the last decades, urging the need for a more effective and harmonized regional and cross‐border cooperation in the field of flood forecasting. Reliable and comprehensive hydrologic data are the basis of flood forecasting. This paper provides an overview of the national flood forecasting systems in the DRB. Detailed information about meteorological and hydrological measurements, flood modelling, forecasting, and flood warnings is provided for 12 countries that cover almost 95% of the total DRB area. Notably, significant differences exist among the countries in terms of the measuring network density, the models used as well as forecasting and warnings methodology. These differences can be attributed to the geographical and climatological setting, political situation, historical forecasting development, etc. It can be seen that there is still much room left for improvements of measurement networks (e.g., density, measured parameters) and models used that could be improved to enhance the flood forecasting in the DRB. [ABSTRACT FROM AUTHOR]

This paper uses Labor Force Survey data for European countries to estimate national investment in data assets, where the asset boundary is extended beyond that for software and databases as currently defined in the System of National Accounts. We find that: (a) in 2011–2018, 1.4 percent of EU‐28 employment was engaged in the formation of (software and) data assets, with a mean growth rate of 5 percent per annum; (b) on average in 2011–2016, expanding the asset boundary raises the level of own‐account GFCF in software and databases in the EU‐16 by 61 percent, and mean growth in real investment in own‐account software and data assets to 6.9 percent pa, compared to 2.7 percent pa in national accounts; (c) in 2011–2016, expansion of the asset boundary raises labor productivity growth in the EU‐13 from 0.79 percent to 0.83 percent pa, and the contribution of software and data capital deepening over three‐fold, from 0.03 percent to 0.10 percent pa. [ABSTRACT FROM AUTHOR]

The author reflects on peer reviewing of scientific papers and unwritten social contract of peer reviewing. Topics discussed include submission of manuscript with diagrammatic representation including main claims, interpretation, wider implications and data supporting the claim, an increase in peer reviewing as more papers are being published, not providing incentives and stopping social contract and transdisciplinary papers which makes it difficult to find editors.

Migration scholars agree that migration is a highly gendered process. While the literature on this topic is increasing, the knowledge produced remains fragmentary and has not been synthesized systematically yet. This literature review aims at summarizing the current findings of quantitative migration research comparing migration patterns between genders and highlighting gaps and patterns in the literature over time. Following a reproducible and systematic approach, 6032 articles have been scanned and 170 were considered for in‐depth content analysis. The review of the literature revealed that women have a lower propensity than men to realize their migration aspirations conditional on migration intentions. Moreover, many articles analyzing migration flows by gender do not support the common narrative of a feminization of migration. Finally, evidence from the migration literature supports the assumption that migrant women experience a double burden of discrimination in the destination country labor market based on their gender and their migration status. It becomes apparent that gender‐based comparisons between migration outcomes have received the most attention in the literature followed by the comparison of determinants. The stage of the journey received only little attention. This literature review also focuses on the data sources used to produce our knowledge on gender differences in the migration process. Thereby, it stands out that the majority of papers rely on survey data for the analysis. Digital trace data are a promising source for gender‐disaggregated data and can potentially complement the scarce aggregate migration data landscape. [ABSTRACT FROM AUTHOR]

The current epoch in fisheries science has been driven by continual advances in laboratory techniques and increasingly sophisticated approaches to analysing datasets. We now have the scientific knowledge and tools to proactively identify obstacles to the sustainable management of marine resources. However, in addition to technological advances, there are predicted global environmental changes, each with inherent implications for fisheries. The 2023 symposium of the Fisheries Society of the British Isles called for "open and constructive knowledge exchange between scientists, stakeholders, managers and policymakers" (https://fsbi.org.uk/symposium-2023/), a nexus of collaborative groups best placed to identify issues and solutions. Arguably, the Centre of Environment, Aquaculture and Fisheries Science (Cefas) and their Scientific Advice for Fisheries Management (SAFM) Team sit at the centre of such a network. SAFM regularly engages with managers and stakeholders, undertakes scientific research, provides fisheries advice to the UK government, and are leading experts within the International Council for the Exploration of the Sea (ICES). As such, this paper is an opinion piece, linked to individual authors specialisms, that aims to highlight emerging issues affecting fisheries and suggest where research efforts could be focused that contribute to sustainable fisheries. [ABSTRACT FROM AUTHOR]

Cupin proteins share a double-stranded b-helix fold, form one of the largest protein superfamilies, and possess remarkable functional diversity. They usually exist in homooligomeric states. Goss and co-workers recently reported that the cupin protein Pac13, which is a dehydratase that mediates the formation of the 3’-deoxy nucleoside of pacidamycins, is an unusual small monomer. However, a careful analysis of the biophysical and structural data provided by the authors suggests that Pac13 is in fact a homodimer, similar to many other cupin proteins. [ABSTRACT FROM AUTHOR]

This paper presents a stage in the research on the development and implementation of a low cost, flexible, and efficient method for assisted testing of foot prostheses, as the case. This paper describes a method for assisted evaluation of three types of foot prostheses standpoint form deviation (hip prosthesis and two osteosynthesis plates for fibula and tibia). The key aspect of the developed method is a software application as virtual instrument programmed in this research stage. Its role is to provide as quickly and objectively as possible all necessary information on the results of testing. The main advantage of the created and applied virtual instrument is that it is flexible and user friendly, so that it can be successfully used in all orthopedic surgeries. This research stage (described) includes the software application programming and also its application to process all measured data in terms of form deviation for all three tested foot implants. [ABSTRACT FROM AUTHOR]

Over 10 million ED visits occur each year across Australia and Aotearoa New Zealand. Outside basic administrative data focused on time‐based targets, there is minimal information about clinical performance, quality of care, patient outcomes, or equity in emergency care. The lack of a timely, accurate or clinically useful data collection represents a missed opportunity to improve the care we deliver each day. The present paper outlines a proposal for a National Acute Care Secure Health Data Environment, including design, possible applications, and the steps taken to date by the Australasian College for Emergency Medicine ED Epidemiology Network in collaboration with the College of Emergency Nursing Australasia. Optimal use of the existing information collected routinely during clinical care of emergency patients has the potential to enable data‐driven quality improvement and research, leading to better care and better outcomes for millions of patients and families each year. [ABSTRACT FROM AUTHOR]

This paper introduces the New Industrial Policy Observatory (NIPO) data set and documents emergent patterns of policy intervention during 2023 associated with the return of industrial policy. The data show that the recent wave of new industrial policy activity is primarily driven by advanced economies and that subsidies are the most employed instrument. Trade restrictions on imports and exports are more frequently used by emerging market and developing economies. Strategic competitiveness is the dominant motive governments give for these measures, but other objectives such as climate change, resilience and national security are on the rise. In exploratory regressions, we find that implemented measures are correlated with the past use of measures by other governments in the same sector, pointing to the tit‐for‐tat nature of industrial policy. Furthermore, domestic political economy factors and macroeconomic conditions correlate with the use of industrial policy measures. We intend for the NIPO to be a publicly available resource to help monitor the evolution and effects of industrial policies. [ABSTRACT FROM AUTHOR]

Should consumer researchers employ silicon samples and artificially generated data based on large language models, such as GPT, to mimic human respondents' behavior? In this paper, we review recent research that has compared result patterns from silicon and human samples, finding that results vary considerably across different domains. Based on these results, we present specific recommendations for silicon sample use in consumer and marketing research. We argue that silicon samples hold particular promise in upstream parts of the research process such as qualitative pretesting and pilot studies, where researchers collect external information to safeguard follow‐up design choices. We also provide a critical assessment and recommendations for using silicon samples in main studies. Finally, we discuss ethical issues of silicon sample use and present future research avenues. [ABSTRACT FROM AUTHOR]

Starting with Tinbergen (1962, Shaping the world economy: Suggestions for an international economic policy, The Twentieth Century Fund), quantifying the effects of regional trade agreements (RTAs) on international trade flows has always been among the most popular topics in the trade literature. Also not surprisingly, to estimate the effects of RTAs, most researchers and policy analysts have relied on the workhorse model of trade— the gravity equation. Over the past 60years, there have been many important developments in the RTA literature, both in terms of better methods to quantify their effects, and in terms of more and higher quality data. The objective of this paper is to trace the evolution of the methods and data developments in the RTA literature, from Tinbergen's very first exploration until today, and to critically evaluate their significance for our ability to measure the impact of RTAs (and other policies) on international trade. [ABSTRACT FROM AUTHOR]

Our complex world requires multivariate reasoning to make sense of reality. Within this paper, we offer a sequence of activities designed to develop multivariate reasoning by explicitly connecting data and visualization. The activities were designed based on a hypothetical learning trajectory we conjectured for students with limited experience with multivariate visualizations. Drawing from evidence collected using these activities in a series of professional development sessions with in‐service teachers, we find that the activities functioned as intended, and thus we promote these activities for developing students' multivariate reasoning at the secondary and post‐secondary level. We detail specific challenges the teachers faced, and based on these results, offer our reflections and recommendations for curricula and teaching. [ABSTRACT FROM AUTHOR]

The last few decades have witnessed technology progress with leaps and bounds leading to the creation and global adoption of different types of digital devices and platforms that can make personalized recommendations to an individual. One of the consequences of the ubiquity of these devices is the daily generation of data in large quantities. This data includes the sensitive data of an individual as well as multinational organizations and therefore, it must be always kept confidential to prevent its theft and misuse by malicious parties. However, the large volumes of data generated make it difficult to create a robust security solution to safeguard the data from different types of cyberattacks. The paper contributes to the data security industry by consolidating numerous data security algorithms that change with the infrastructure that the data is amid and also outlines the regulations surrounding it. This review paper also aims to highlight the benefits and drawbacks of the security algorithms proposed by researchers to encourage further discussion and consideration of the algorithms for their potential implementation in appropriate domains and also inspire them to develop more powerful and robust security algorithms to cover the drawbacks of the existing ones. [ABSTRACT FROM AUTHOR]

Unlabelled: Bibliometric data published by the Institute of Scientific Information in Philadelphia (ISI), and which was previously discussed in Acta Paediatrica, has increasingly been used despite all the relevant and severe criticism that has been raised against this method of evaluating individual research results and grading scientific journals. It is obvious that the present trend regarding the use of bibliometric data as a basis for priorities and funding of research and for the promotion of individual scientists favours American-oriented research projects at the expense of those that are based on concepts of predominantly European relevance.Conclusion: For the future of non-American research, it is important that no single super-power, i.e. the USA, should dominate scientific priorities. The condition for efficient European competition is that European Centres with high levels of competence for creative research and training of scientists from all over the world are established. In addition, it is important that the results of European research are published in prestigious European journals, as was the situation before World War II. [ABSTRACT FROM AUTHOR]

Accurate and comprehensive diatomic molecular spectroscopic data have long been vital in a wide variety of applications for measuring and monitoring astrophysical, industrial, and other gaseous environments. These data are also used extensively for benchmarking quantum chemistry and applications from quantum computers, ultracold chemistry and the search for physics beyond the standard model. Useful data can be highly detailed like line lists or summative like molecular constants, and obtained from theory, experiment, or a combination. There are plentiful (though not yet sufficient) data available, but these data are often scattered. For example, molecular constants have not been compiled since 1979 despite the existing compilation still being cited more than 200 times annually. Furthermore, the data are interconnected but updates in one type of data are not yet routinely applied to update interconnected data: in particular, new experimental and ab initio data are not routinely unified with other data on the molecule. This paper provides information and strategies to strengthen the connection between data producers (e.g., ab initio electronic structure theorists and experimental spectroscopists), data modelers (e.g., line list creators and others who connect data on one aspect of the molecule to the full energetic and spectroscopic description) and data users (astronomers, chemical physicists, etc.). All major data types are described including their source, use, compilation, and interconnectivity. Explicit advice is provided for theoretical and experimental data producers, data modelers, and data users to facilitate optimal use of new data with appropriate attribution. This article is categorized under:Theoretical and Physical Chemistry > Spectroscopy [ABSTRACT FROM AUTHOR]

Current wildlife management practices rely largely on quantitative data to legitimise decisions, manage human–wildlife conflicts and control wildlife populations. This paper draws attention to the affective relationships between humans and animals inevitably formed in the practice of producing these data. Based on fieldwork that explores wild boar management in the Veluwe, the Netherlands, we demonstrate the significance of these affective encounters. Specifically, we develop an understanding of mindedness that draws on processes of affective learning in wildlife management practices. To understand this mindedness and how it emerges in wild boar management practices, we use the concepts of affect, attunement and animal subjectivities. First, we show how the numero‐politics involved in wildlife management presumes animal minds to be static and generically defined by species, and their presence and behaviour to be context independent. Subsequently, we describe the entanglements of humans, wild animals and the landscape, aiming to produce an appreciation of the mutuality that is involved in knowing and conserving wildlife. This, we propose, helps to demonstrate how various – individual or collective – forms of human and non‐human mindedness are implicated in management practices but remain invisible and underappreciated in formal accounts. We conclude by explicating a multinatural approach to the management of wildlife that explicitly builds on an acknowledgment of mindedness as a feature of individuals, collectives and landscapes. This paper explicates a multinatural approach to wildlife management that builds on an acknowledgment of mindedness as a feature of individuals, collectives and landscapes. We explored wild boar management in the Veluwe, the Netherlands. [ABSTRACT FROM AUTHOR]

The adoption of citizen science methodologies by environmental organisations and ecologists entrusts the task of collecting ecological data to non‐experts operating at a local scale. This presents individuals and communities with opportunities to monitor ecological change and contribute to local environmental management. Little is known about why volunteers choose to participate in burgeoning contemporary citizen science research initiatives. The aim of this paper is thus to explore volunteer motivation for involvement in two environmental citizen science initiatives, based in the United Kingdom. It contributes to understandings of the socio‐geographical influences that act on participation in environmental citizen science. It is proposed within this paper that affective connections with local geographies provide a conceptual framework for understanding citizen science motivations. The paper discusses the main themes emerging from site‐based, in‐depth interviews with 22 citizen science participants in various UK locations. The study revealed that early affective bonds formed with ecological spaces endured throughout life courses, while citizen science participation offered a way of remaining connected to local environments. The paper reflects on the endurance of affective environmental bonds and their manifestation within the expressed motivations for citizen science participation, which emerged as fulfilling a compulsion to observe ecological surroundings, a desire to participate in environmental research and a commitment to protecting local environments. The paper proposes that citizen science participation offers a framework to connect to and protect local and global affinity spaces, while assisting in monitoring global environmental change. Little is known about why volunteers choose to participate in burgeoning contemporary citizen science research initiatives, this paper's aim is to explore volunteer motivation for involvement in two environmental citizen science initiatives, based in the United Kingdom. It is proposed that affective connections with local geographies provide a conceptual framework for understanding citizen science motivations. The paper reflects on the endurance of affective environmental bonds and their manifestation within the expressed motivations for citizen science participation, which emerged as fulfilling a compulsion to observe ecological surroundings; a desire to participate in environmental research and a commitment to protecting local environments. [ABSTRACT FROM AUTHOR]

Interdisciplinary data fuel fisheries oceanography research and the ecosystem‐based approaches to management and sustainable development it informs. Underlying this is a distributed ocean observing framework that is integrated, interoperable, interactive, and accessible. In recognition of the 30th anniversary of Fisheries Oceanography the journal, this paper reviews the evolution of observing instruments and platforms used in contemporary fisheries oceanography the science. Illustrated with personal anecdotes, past efforts to create or adopt observing technologies, and examples of their use in research, this highlights the spectrum of instruments, systems, and programs used to survey and monitor ocean ecosystems. Modern ocean observing systems are complex and varied, reflecting the range and diversity of data required by fisheries oceanographers. These systems require a large and ongoing investment and an interdisciplinary community of scientists, engineers, and technicians to design, build, install, operate, and maintain them. Common themes emerge from a review of past successful instrument R&D and deployments. It is a highly collaborative, integrative, and iterative process. Most systems are the result of vision, planning, and perseverance, backed by careful calibration and intercomparison. Long‐term support is essential; public–private partnerships that leverage funding, technology, and infrastructure are critical. Sustaining long time series for monitoring population and ecosystem change and to support fisheries oceanography research is a priority. Future areas of focus include continuously innovating and updating technologies, implementing a backbone of core observations, and maintaining a nimble infrastructure and R&D capacity to seize new opportunities and address emerging challenges. [ABSTRACT FROM AUTHOR]

This paper presents a large-scale experimentation of the smart sewage system, which is conducted at the Campus of Lille University. The campus stands for a town of about 25 000 users with 150 buildings and 100 km urban networks. The experimentation is carried out within a large-scale demonstrator of the Smart City, which includes a smart monitoring of the drinking water, sewage, electrical and district heating networks. The originality of this paper lies in the integrated approach, which includes smart monitoring as well as data collection and analysis. The monitoring of the drinking water and sewage networks allows the use of numerical modelling of and the detection of eventual connection between the sewage and storm water systems. The paper shows how the smart technology could be implemented and used to enhance our understanding of the sewage system and to improve its management. [ABSTRACT FROM AUTHOR]

This research starts its journey from a video that failed to be published during the 2009 uprising in Iran. By following data outside the common trajectories of data circulation in the global North, this paper offers new geographical and data imaginations neglected by the universalised understandings of data and its political economy. Consequently, data's behaviour as a thing is thoroughly investigated in the "follow the thing tradition" by scrutinising data as a commodity, its meanings and its associations. Using actor‐network‐theory, the paper highlights data's open and contested character as well as the breakdowns throughout its journey. Following an uncirculated video via its traces sheds light on data's agency in evoking different assemblages and spatialities. It also reflects on the epistemological importance of not treating the Southern data as exceptional and calls for a theoretical landscape that does not leave many realities of data out in its homogenised universal narrative. [ABSTRACT FROM AUTHOR]

The practice of modern craniomaxillofacial surgery has been defined by emergent technologies allowing for the acquisition, storage, utilization, and transfer of massive amounts of sensitive and identifiable patient data. This alone has thrust providers into an unlikely and unprecedented role as the stewards of vast databases of digital information. This data powers the potent surgical tool of virtual surgical planning, a method by which craniomaxillofacial surgeons plan and simulate procedural outcomes in a digital environment. Further complicating this new terrain is the involvement of third‐party contractors—a necessary presence in bringing raw data to bear in the office, virtual space, and operating room. The individual privileges and responsibilities of patients, providers, and vendors towards data are situated within the most recent U.S. court rulings and regulations. This paper offers guidance for overseeing the safe and responsible transfer to third‐party contractors, and provides suggestions for negotiating the trinary relationship between physicians, their patients, and the vendors offering this transformative technology. [ABSTRACT FROM AUTHOR]

This paper develops a theory of randomization tests under an approximate symmetry assumption. Randomization tests provide a general means of constructing tests that control size in finite samples whenever the distribution of the observed data exhibits symmetry under the null hypothesis. Here, by exhibits symmetry we mean that the distribution remains invariant under a group of transformations. In this paper, we provide conditions under which the same construction can be used to construct tests that asymptotically control the probability of a false rejection whenever the distribution of the observed data exhibits approximate symmetry in the sense that the limiting distribution of a function of the data exhibits symmetry under the null hypothesis. An important application of this idea is in settings where the data may be grouped into a fixed number of 'clusters' with a large number of observations within each cluster. In such settings, we show that the distribution of the observed data satisfies our approximate symmetry requirement under weak assumptions. In particular, our results allow for the clusters to be heterogeneous and also have dependence not only within each cluster, but also across clusters. This approach enjoys several advantages over other approaches in these settings. [ABSTRACT FROM AUTHOR]

Summary: This paper develops a method to estimate the U.S. output gap by exploiting the cross‐sectional variation of state‐level output and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state's output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. I estimate the model with Bayesian methods using quarterly data from 2005:Q1 to 2018:Q2 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about negative 4.6% around the years of the Great Recession and was about 0.9% in 2018:Q2. [ABSTRACT FROM AUTHOR]

This paper uses survey‐based data for 16 South Korean regions to refine the application of Flegg's location quotient (FLQ) and its variant, the sector‐specific FLQ (SFLQ). These regions vary markedly in terms of size. Especial attention is paid to the problem of choosing appropriate values for the unknown parameter δ in these formulae. Alternative approaches to this problem are evaluated and tested. Our paper adds to earlier research that aims to find a cost‐effective way of adapting national coefficients, so as to produce a satisfactory initial set of regional input coefficients for regions where survey‐based data are unavailable. [ABSTRACT FROM AUTHOR]

This reflection, based on a keynote address to celebrate the fortieth anniversary of Disasters on 14 September 2017, begins by considering some of the prevailing assumptions that existed at the time of the journal's creation. Next is a summary of significant milestones during the past 40 years, covering major global trends, changing disaster impacts, and key developments in disaster risk management. Contrasting approaches in the first and fourth decade of the journal's history are then followed by examples of changes in terminology in the disaster field as an indication of shifting values. The paper goes on to explore the context of 2017, with observations of problem areas such as the loss of knowledge, failures to tackle the root causes of disasters, shortfalls in international assistance, and some negative attitudes. It closes by evaluating some current positive occurrences, including a reduction in casualties, sharing of experience, technological advances, and dramatic improvements in disaster warning. [ABSTRACT FROM AUTHOR]

Purpose: Dynamic positron emission tomography (PET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. Information of different functional regions based on an accurate reconstruction of the activity images and kinetic parametric images has been widely studied and can be useful in research and clinical setting for diagnosis and other quantitative tasks. In this paper, our purpose is to present a novel framework for estimating the kinetic parametric images directly from the raw measurement data together with a simultaneous segmentation accomplished through kinetic parameters clustering. Method: An iterative framework is proposed to estimate the kinetic parameter image, activity map and do the segmentation simultaneously from the complete dynamic PET projection data. The clustering process is applied to the kinetic parameter variable rather than to the traditional activity distribution so as to achieve accurate discrimination between different functional areas. Prior information such as total variation regularization is incorporated to reduce the noise in the PET images and a sparseness constraint is integrated to guarantee the solution for kinetic parameters due to the over complete dictionary. Alternating direction method of multipliers (ADMM) method is used to solve the optimization problem. The proposed algorithm was validated with experiments on Monte Carlo‐simulated phantoms and real patient data. Symbol error rate (SER) was defined to evaluate the performance of clustering. Bias and variance of the reconstruction activity images were calculated based on ground truth. Relative mean square error (MSE) was used to evaluate parametric results quantitatively. Result: In brain phantom experiment, when counting rate is 1 × 106, the bias (variance) of our method is 0.1270 (0.0281), which is lower than maximum likelihood expectation maximization (MLEM) 0.1637 (0.0410) and direct estimation without segmentation (DE) 0.1511 (0.0326). In the Zubal phantom experiment, our method has the lowest bias (variance) 0.1559 (0.0354) with 1 × 105 counting rate, compared with DE 0.1820 (0.0435) and MLEM 0.3043 (0.0644). As for classification, the SER of our method is 18.87% which is the lowest among MLEM + k‐means, DE + k‐means, and kinetic spectral clustering (KSC). Brain data with MR reference and real patient results also show that the proposed method can get images with clear structure by visual inspection. Conclusion: In this paper, we presented a joint reconstruction framework for simultaneously estimating the activity distribution, parametric images, and parameter‐based segmentation of the ROIs into different functional areas. Total variation regularization is performed on the activity distribution domain to suppress noise and preserve the edges between ROIs. An over complete dictionary for time activity curve basis is constructed. SER, bias, variance, and MSE were calculated to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An interview transcript can be a rich source of geographical references whose potential are not always fully realised in their conventional analysis. Geo‐referencing techniques can be used to assign a spatial footprint to place names, adding value to these data and allowing the geographic information within them to be exploited when coupled with GIS technology. This paper discusses a method of analysing and visualising interview transcripts in order to understand the spatial extent of public policy practitioners' activities. Through aggregation and statistical mapping it is possible to gain insight into the importance of space across a range of public policy themes and to understand the relationship between practitioner‐defined policy themes and the formal administrative boundaries within which they typically work. The research demonstrates that spatial working practices rarely conform to formal administrative boundaries and that there are varying degrees of spatial focus between different policy themes within localities. It also reveals that spatial working practices can continue to be influenced by historic geographies and can be pulled in different directions, reflecting both the devolved nature of the sector and the particular geographical context of the setting. It concludes that mapping the interview transcript can add value and provide additional insights to more conventional analysis. [ABSTRACT FROM AUTHOR]

The article discusses various topics published within the issue including the microdata on expenditure, the data quality in traditional budget and the groundbreaking attempt in collecting information on the allocation household consumption.

We propose a new data source (Google Scholar) and metric (Hirsch's h-index) to assess journal impact in the field of economics and business. A systematic comparison between the Google Scholar h-index and the ISI Journal Impact Factor for a sample of 838 journals in economics and business shows that the former provides a more accurate and comprehensive measure of journal impact. [ABSTRACT FROM AUTHOR]

Human research protection programs (HRPP) generate an abundance of data on performance, capacity, and compliance. When used effectively, this information can be instrumental in helping HRPPs meet programmatic and institutional goals, demonstrate growth and success, and improve the HRPP overall. Metrics must be grounded in professional insight so that HRPPs can pair analytics with strategies for future action or improvement. The purpose of this paper is to demonstrate how high‐performing HRPPs develop, adopt, and implement a metrics framework that benefits everyday operations and produces real‐world results. Through a three‐part thematic framework (of insight, data, and action) and by providing case examples and actionable strategies, this article will address how HRPPs iteratively develop and characterize their metrics, build a metrics framework that leverages both quantitative and qualitative data to validate outcomes, and activate human insight to produce meaningful communication, visualization, and dissemination of data. [ABSTRACT FROM AUTHOR]

This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art. [ABSTRACT FROM AUTHOR]

Abstract: Due to increasingly strict environmental regulation of marine transportation, vessel operators and other stakeholders are required to evaluate feasible compliance measures in the face of multiple criteria and with attention to uncertainties and risks. Several methods and models within operations research have been applied to explore such decision contexts, but little is reported on the problem structure itself and the key values, concerns, and uncertainties that apply to them. The objective of the paper is to present a problem structure for acquisition of marine emission reduction technologies in the Norwegian ferry fleet drawing on methods from decision science and systems engineering. To attain this objective, we utilize the SPADE methodology, which details five problem‐solving activities covering stakeholders, problem formulation, alternatives, decisions, and continuous evaluation. Each activity is informed by data collected through stakeholder interviews and literature analysis to establish an initial representation of acquisition decision issues. To keep a consistent and traceable problem structure, we provide a stakeholder diagram, value network, systemigram, and decision hierarchy centered around stakeholders and their values. These models may serve to inform decision‐makers in the development and appraisal of emission reduction technologies and strategies. The paper demonstrates the application of systems engineering as a problem‐structuring framework for complex, multidimensional marine technology acquisition decisions. [ABSTRACT FROM AUTHOR]

To identify poor households, the government of Vietnam applies a combination of proxy means tests and quick collection of income data. This paper examines how well the government's poverty identification reaches the really poor in Vietnam. It is found that there is a large difference between the poverty rate for provinces and districts reported by the government and the rates estimated using expenditure and income data from independent household surveys. There is also a large difference between the poverty status of households identified by local authorities and the poverty status identified by income or expenditure data. More than 50 per cent of the poor households identified by local authorities are not poor in terms of income or expenditure measures. A better identification approach would be to use only proxy means tests and not income data collected using the simple questionnaire. [ABSTRACT FROM AUTHOR]

Digitisation offers significant opportunities for the formulated product industry to transform the way it works. Recent developments in the fields of artificial intelligence, machine learning, robotics and high performance computing present disruptive and transformative methods to accelerate the R&D process. These methods include physics‐based chemical simulation, data driven models and hybrid approaches, each of which has been shown to provide new physical and chemical insights that can augment experimental R&D. However, successfully exploiting these technologies in R&D organisations at scale is challenging due to the high level of domain specialisation required and the need for novel models for the formulation domain. In this paper we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. These demonstrators focus on computational modelling methods. In particular we discuss how we tackled the issue of domain specialisation and the challenges we encountered in creating accurate models for formulation problems. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]