Ensuring the Data-Rich Future of the Social Sciences

Using freedom of information requests to access novel data sources in health professions education research

Educators and researchers are reliant upon access to data to drive teaching methods, curricular improvements, and progress in medical education research. However, data are not always accessible, due to resource constraints, institutional policies, and privacy concerns. Researchers have attempted to access novel data sources through surveys, semistructured interviews, and databases; however, these methodologies are limited. To improve access to data, Freedom of Information (FOI) Acts grant researchers the ability to formally request data that any public institute holds. Researchers have been reluctant to use this tool due to negative perceptions, despite its unique benefits. To increase awareness of this underutilized methodology, we summarize the process of FOI Act requests, its strengths and weaknesses, and the ways in which health professions education can leverage FOI requests within research. We provide examples of the use of FOI requests as a research method within adjacent fields and nascent use within the field of health professions research. In doing so, we hope to highlight how FOI requests can be a useful tool in health professions education researchers and its potential to increase access to unique data sources.

Publishing publicly available interview data: an empirical example of the experience of publishing interview data

In September 2021 I made a collection of interview transcripts available for public use under a CreativeCommons license through the Princeton DataSpace. The interviews include 39 conversations I had with gig workers at AmazonFlex, Uber, and Lyft in 2019 as part of a study on automation efforts within these organizations. I made this decision because (1) I was required to contribute to a publicly available data set as a requirement of my funding and (2) I saw it as an opportunity to engage in the collaborative qualitative science experiments emerging in Science and Technology studies. This article documents my thought process and step-by-step design decisions for designing a study, gathering data, masking it, and publishing it in a public archive. Importantly, once I decided to publish these data, I determined that each choice about how the study would be designed and implemented had to be assessed for risk to the interviewee in a very deliberate way. It is not meant to be comprehensive and cover every possible condition a researcher may face while producing qualitative data. I aimed to be transparent both in my interview data and the process it took to gather and publish these data. I use this article to illustrate my thought process as I made each design decision for this study in hopes that it could be useful to a future researcher considering their own data publishing process.

Global environmental equities and investor sentiment: the role of social media and Covid-19 pandemic crisis

According to researchers, information generated from social media provides useful data for understanding the behaviour of various types of financial assets, using the sentiment expressed by these network users as an explanatory variable of asset prices. In a context in which investment based on sustainability and environmental preservation values is vital, there is no known scientific work that analyses the relationship between social networks and environmental investment, which is closely related to the 2030 Agenda for Sustainable Development. In this study, we aim to identify how investor sentiment, generated from social networks, influences environmental investment and whether this influence depends on the time variable, as well the role of the pandemic crisis and the Russia-Ukraine war. Our results show different forms of behaviour for the different periods considered, with the proximity between the two types of variables being time-varying. For shorter periods, proximity occurred mainly during the pandemic crisis, repeatedly revealing that sentiment is a risk factor in environmental investment and in particular how important the information generated from social networks can be in pricing environmental assets. For longer periods, no common stochastic trends were identified. The mechanisms generating the series are thus characterised by a certain autonomy.

Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches

Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the “gold standard” of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels—for example, a summary of the work and the reviewer's appraisal—but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)—which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.

The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information

The scholarly knowledge ecosystem presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale. This article draws upon significant reports on aspects of the ecosystem to characterize the most important research challenges and promising potential approaches. The focus of this review article is the fundamental scientific research challenges related to developing a better understanding of the scholarly knowledge ecosystem. Across a range of disciplines, we identify reports that are conceived broadly, published recently, and written collectively. We extract the critical research questions, summarize these using quantitative text analysis, and use this quantitative analysis to inform a qualitative synthesis. Three broad themes emerge from this analysis: the need for multi-sectoral cooperation and coordination, for mixed methods analysis at multiple levels, and interdisciplinary collaboration. Further, we draw attention to an emerging consensus that scientific research in this area should by a set of core human values.

Query-based-learning mortality-related decoders for the developed island economy

Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak. Recent studies showed Wiener Model shares merits of interpretability, implementation, and adaptation to nonlinear fluctuation in terms of real-time decoding. Previous work reported Google Trends effectively predicts death-related trends for the continent economy, yet whether it applies to the island economy is unclear. To this end, a framework of the mortality-related model for a developed island economy Taiwan was built based on potential death causes from Google Trends, aiming to provide new insights into death-related online search behavior at a population level. Our results showed estimated trends based on the Wiener model significantly correlated to actual trends, outperformed those with multiple linear regression and seasonal autoregressive integrated moving average. Meanwhile, apart from that involved all possible features, two other sets of feature selecting strategies were proposed to optimize pre-trained models, either by weights or waveform periodicity of features, resulting in estimated death-related dynamics along with spectrums of risk factors. In general, high-weight features were beneficial to both "die" and "death", whereas features that possessed clear periodic patterns contributed more to "death". Of note, normalization before modeling improved decoding performances.

Open data: The building block of 21st century (open) science

This paper identifies the potential benefits of data sharing and open science, supported by artificial intelligence tools and services, and dives into the challenges to make data open and findable, accessible, interoperable, and reusable (FAIR).

Role of big geospatial data in the COVID-19 crisis

The outbreak of the 2019 novel coronavirus disease (COVID-19) has infected 4 million people worldwide and has caused more than 300,000 deaths worldwide. With infection and death rates on rise, COVID-19 poses a serious threat to social functioning, human health, economies, and geopolitics. Geographic information systems and big geospatial technologies have come to the forefront in this fight against COVID-19 by playing an important role by integrating multisourced data, enhanced and rapid analytics of mapping services, location analytics, and spatial tracking of confirmed, forecasting transmission trajectories, spatial clustering of risk on epidemiologic levels, public awareness on the elimination of panic spread and decision-making support for the government and research institutions for effective prevention and control of COVID-19 cases. Big geospatial data has turned itself as the major support system for governments in dealing with this global healthcare crisis because of its advanced and innovative technological capabilities from preparation of data to modeling the results with quick and large accessibility to every spatial scale. This robust data-driven system using the accurate and prediction geoanalysis is being widely used by governments and public health institutions interfaced with both health and nonhealth digital data repositories for mining the individual and regional datasets for breaking the transmission chain. Profiling of confirmed cases on the basis of location and temporality and then visualizing them effectively coupled with behavioral and critical geographic variables such as mobility patterns, demographic data, and population density enhance the predictive analytics of big geospatial data. With the intersection of artificial intelligence, geospatial data enables real-time visualization and syndromic surveillance of epidemic data based on spatiotemporal dynamics and the data are then accurately geopositioned. This chapter aims to reflect on the relevance of big geospatial data and health geoinformatics in containing and preventing the further spread of COVID-19 and how countries and research organizations around the world have used it as accurate, fast, and comprehensive dataset in their containing strategy and management of this public health crisis. China and Taiwan are used as case studies as in how these countries have applied the computational architecture of big geospatial data and location analytics surveillance techniques for prediction and monitoring of COVID-19-positive cases.

Work Engagement Among Public Employees: Antecedents and Consequences

This study is an investigation of the relationships among job meaningfulness, work engagement, and performance, including testing for a possible mediation effect of work engagement on the relationship between job meaningfulness and performance. We examine task interdependence as a boundary condition that facilitates employee engagement using two-stage multiple-source respondent data drawn from a sample of 183 Uzbek employees from public organizations and their 47 supervisors to test the hypotheses. The research findings confirm a positive association between job meaningfulness and engagement and the relationship between work engagement and performance. Mediation analysis using bootstrapping indicated that work engagement explained the influence of meaningfulness on performance. Furthermore, task interdependence negatively moderated the relationship between meaningfulness and engagement. This study responds to calls for researchers to identify the key and situational drivers of work engagement as well as examine the importance of meaningfulness in the public sector. It also increases the external validity of the findings by examining the relationship between engagement and performance in a non-Western context, namely, Islamic Uzbekistan. Despite the limitations of this research, the empirical findings contribute to the growing body of research on work engagement and meaningfulness in public organizations.

The changing face of MedEdPublish

MedEdPublish has come a long way since it was launched in 2016 by AMEE as an independent academic e-journal that supports scholarship in health professions education. Beginning as a relatively small, in-house publication on a web platform adapted for the purpose, we invited members of our community of practice to submit articles on any topic in health professions education, and encouraged a wide range of article types. All articles were published so long as they met editing criteria and where within scope. Reviews were welcomed from both members of our Review panel and the general readership, all published openly with contributors identified. Many articles attracted several reviews, responses and comments, creating interactive discussion threads that provided learning opportunities for all. The outcome surpassed our expectations, with over 500 articles submitted during 2020, beyond the capacity of our editing team and platform to achieve our promise of rapid publishing. We have now moved to a much larger and powerful web platform, developed by F1000 Research and within the Taylor and Francis stable, the home of AMEE’s other journal, Medical Teacher. Most of our innovations are supported by the new platform and there is scope for further developments. We look forward to an exciting new phase of innovation, powered by the F1000 platform.

Active Curation of Large Longitudinal Surveys: A Case Study

In this paper we take an in-depth look at the curation of a large longitudinal survey and activities and procedures involved in moving the data from its generation to the state that is needed to conduct scientific analysis. Using a case study approach, we describe how large surveys generate a range of data assets that require many decisions well before the data is considered for analysis and publication. We use the notion of active curation to describe activities and decisions about the data objects that are “live,” i.e., when they are still being collected and processed for the later stages of the data lifecycle. Our efforts illustrate a gap in the existing discussions on curation. On one hand, there is an acknowledged need for active or upstream curation as an engagement of curators close to the point of data creation. On the other hand, the recommendations on how to do that are scattered across multiple domain-oriented data efforts. In describing the complexities of active curation of survey data and providing general recommendations we aim to draw attention to the practices of active curation, stimulate the development of interoperable tools, standards, and techniques needed at the initial stages of research projects, and encourage collaborations between libraries and other academic units.

Computational Reproducibility: A Practical Framework for Data Curators

Introduction: This paper presents concrete and actionable steps to guide researchers, data curators, and data managers in improving their understanding and practice of computational reproducibility. Objectives: Focusing on incremental progress rather than prescriptive rules, researchers and curators can build their knowledge and skills as the need arises. This paper presents a framework of incremental curation for reproducibility to support open science objectives. Methods: A computational reproducibility framework developed for the Canadian Data Curation Forum serves as the model for this approach. This framework combines learning about reproducibility with recommended steps to improving reproducibility. Conclusion: Computational reproducibility leads to more transparent and accurate research. The authors warn that fear of a crisis and focus on perfection should not prevent curation that may be ‘good enough.’

A Robust Approach for Identifying the Major Components of the Bribery Tolerance Index

The paper aims to emphasize the advantages of several advanced statistical and data mining techniques when applied to the dense literature on corruption measurements and determinants. For this purpose, we used all seven waves of the World Values Survey and we employed the Naive Bayes technique in SQL Server Analysis Services 2016, the LASSO package together with logit and melogit regressions with raw coefficients in Stata 16. We further conducted different types of tests and cross-validations on the wave, country, gender, and age categories. For eliminating multicollinearity, we used predictor correlation matrices. Moreover, we assessed the maximum computed variance inflation factor (VIF) against a maximum acceptable threshold, depending on the model’s R squared in Ordinary Least Square (OLS) regressions. Our main contribution consists of a methodology for exploring and validating the most important predictors of the risk associated with bribery tolerance. We found the significant role of three influences corresponding to questions about attitudes towards the property, authority, and public services, and other people in terms of anti-cheating, anti-evasion, and anti-violence. We used scobit, probit, and logit regressions with average marginal effects to build and test the index based on these attitudes. We successfully tested the index using also risk prediction nomograms and accuracy measurements (AUCROC > 0.9).

Modelling urban vibrancy with mobile phone and OpenStreetMap data

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people’s location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy.

Quantifying the differences in call detail records

The increasing availability of mobile phone data has attracted the attention of several researchers interested in studying our collective behaviour. Our interactions with the phone network can take several forms, from SMS messages to phone calls and data usage. Typically, mobile phone data are released to researchers in the form of call detail records, which contain records of different types of interactions, and can be used to analyse various aspects of our behaviour. However, the inherently behavioural nature of these interactions may result in differences between how we make phone calls and receive text messages. Studies which rely on data derived from these interactions, therefore, need to carefully consider these differences. Here, we aim to investigate differences and limitations of different types of mobile phone interactions data by analysing a large mobile phone dataset. We study the relationship between different types of interactions and show how it changes over time. We anticipate our findings to be of interest to all researchers working in the area of computational social science.

An empirical study of research ethics and their role in psychologists’ data sharing intentions using consequentialism theory of ethics

The purpose of this study is to examine how different ethical dimensions of egoism, utilitarianism, and deontology all help in the formation of psychologists’ research ethics for data sharing, and how the research ethics eventually affect psychologists making decisions regarding whether to engage in data sharing. This research utilized consequentialism theory of ethics as its theoretical framework to develop its research model of psychologists’ data sharing as mediated by research ethics. It conducted an online survey with psychologists in US academic institutions and collected a total of 362 valid responses. Then, it employed the structural equation modeling technique to evaluate the research model and related hypotheses of psychologists’ data sharing intentions as mediated by the profession’s research ethics. This research found that perceived career benefit, perceived community benefit, and norm of data sharing all significantly contribute to the formation of psychologists’ research ethics for data sharing, and then these research ethics, along with perceived community benefit and norm of data sharing, significantly influence psychologists’ data sharing intentions. This study suggests that the consequentialism theory of ethics nicely explains psychologists’ formation of their research ethics for data sharing and their decision to engage in data sharing. The study also suggests that research communities can better promote researchers’ data sharing behaviors by stimulating their research ethics through different ethical dimensions, including egoism (career benefit), utilitarianism (community benefit), and deontology (norm of data sharing).

Data science in organizations: Conceptualizing its breakthroughs and blind spots

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

Improving data access democratizes and diversifies science

The foundation of the scientific method rests on access to data, and yet such access is often restricted or costly. We investigate how improved data access shifts the quantity, quality, and diversity of scientific research. We examine the impact of reductions in cost and sharing restrictions for satellite imagery data from NASA's Landsat program (the longest record of remote-sensing observations of the Earth) on academic science using a sample of about 24,000 Landsat publications by over 34,000 authors matched to almost 3,000 unique study locations. Analyses show that improved access had a substantial and positive effect on the quantity and quality of Landsat-enabled science. Improved data access also democratizes science by disproportionately helping scientists from the developing world and lower-ranked institutions to publish using Landsat data. This democratization in turn increases the geographic and topical diversity of Landsat-enabled research. Scientists who start using Landsat data after access is improved tend to focus on previously understudied regions close to their home location and introduce novel research topics. These findings suggest that policies that improve access to valuable scientific data may promote scientific progress, reduce inequality among scientists, and increase the diversity of scientific research.

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

In recent years, many studies have used social media data to make estimates of electoral outcomes and public opinion. This paper reports the findings from a meta-analysis examining the predictive power of social media data by focusing on various sources of data and different methods of prediction; i.e., (1) sentiment analysis, and (2) analysis of structural features. Our results, based on the data from 74 published studies, show significant variance in the accuracy of predictions, which were on average behind the established benchmarks in traditional survey research. In terms of the approaches used, the study shows that machine learning-based estimates are generally superior to those derived from pre-existing lexica, and that a combination of structural features and sentiment analyses provides the most accurate predictions. Furthermore, our study shows some differences in the predictive power of social media data across different levels of political democracy and different electoral systems. We also note that since the accuracy of election and public opinion forecasts varies depending on which statistical estimates are used, the scientific community should aim to adopt a more standardized approach to analyzing and reporting social media data-derived predictions in the future.

Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century

Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective.

Employee voice, engagement and organizational effectiveness: a mediated model

Purpose

This study is based on social exchange theory and aims at understanding the role of employee engagement as a mediator between employee voice and organizational effectiveness.

Design/methodology/approach

Data was collected to test the mediating role of employee engagement between employee voice and organizational effectiveness. The respondents were employees in different IT companies located in major cities in India. The model was tested for full and partial mediation of employee engagement using structural equation modeling.

Findings

Considering the self-reported survey from 232 employees from companies in the IT sector, the findings reveal that there exists a significant association between employee voice and organizational effectiveness. The results reflect a close association between employee engagement and organizational effectiveness too. However, no significant association was found between employee voice and organizational effectiveness. Employee engagement is found to mediate the relationship between employee voice and organizational effectiveness.

Research limitations/implications

The foremost limitation of the study is the sample group that is limited to employees working in IT companies in Bangalore city. The results cannot be generalized to the entire IT industry in India. Although attempts are made to eliminate common method bias, there are chances of an overstated relationship by common method variance that cannot be neglected completely.

Practical implications

The paper will provide a deep insight to the practitioners about the role of employee voice in the engagement of employees. It will also indicate to the managers how the effectiveness of an organization can be heightened by creating opportunities for employees to voice their opinion in the organization.

Originality/value

The present study indicated that though there is an association between the independent variable, employee voice, and the dependent variable, organizational effectiveness, the relationship becomes more significant in the presence of employee engagement between them.

Measuring the Psychobiological Correlates of Daily Experience in Adolescents

Mapping the psychobiological correlates of social contexts, experiences, and emotional responses of adolescents in their daily lives provides insight into how adolescent well-being shapes, and is shaped by, experience. Measures of these psychobiological correlates are enabled by devices and technologies that must be precise and suitable for adolescent participants. The present report reviews the most often used research measures, and suggests strategies for best practice, drawn from practical experience. The rapid advances in technological methods to collect attuned measures of psychological processes, social context, and biological function indicate the promise for multimodal measures in ecological settings. Attaining these methodological goals will support research to secure comprehensive, quality data, and advance the understanding of psychobiological function in ambulatory settings.

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. "For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated." -Ursula Franklin.

Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems.

Employee engagement and job performance in Lebanon: the mediating role of creativity

Purpose

The purpose of this paper is to explore the relationship between employee engagement and job performance in the country of Lebanon, and to test whether creativity mediates the relationship between engagement and performance.

Design/methodology/approach

The research sample consisted of 186 respondents working in Lebanese firms. The questionnaire included established measures relating to employee engagement, job performance and creativity – in addition to various demographic questions. Stepwise multiple regression and bootstrapping methods were employed in the analysis of the data.

Findings

The findings showed a significant positive effect of employee engagement on job performance. However, mediation analysis using bootstrapping methods has shown that creativity has fully mediated the relationship between engagement and performance.

Originality/value

The study extends previous research and increases the external validity of the findings by investigating the relationship between engagement and performance in new non-western contexts. Moreover, this is one of the first research studies that explores the role of creativity in the relationship between the two variables; this helps in improving our understanding of the model and aids in enhancing the effect of engagement on performance.

SolarView: Georgia Solar Adoption in Context

The local-national gap is a problem currently plaguing the adoption of emerging technologies targeted at resolving energy transition issues that are characterized by disparities in the adoption of innovations and policies on a local level in response to national policy implementation. These disparities reflect a complex system of technical, economic, social, political, and ecological factors linked to the perceptions held by communities and how they see energy development and national/global policy goals. This dataset is an attempt to bridge the local-national gap regarding solar PV adoption in the State of Georgia (U.S.) by aggregating variables from seven different publicly-available sources. The objective of this activity was to design a resource that would help researchers interested in the context underlying solar adoption on the local scale of governance (e.g., the county level). The SolarView database includes information necessary for informing policy-making activities such as solar installation information, a historical county zip code directory, county-level census data, housing value indexes, renewable energy incentive totals, PV rooftop suitability percentages, and utility rates. As this is a database from multiple sources, incomplete data entries are noted.

An entropic barriers diffusion theory of decision-making in multiple alternative tasks

We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.

Decision-making has been studied in great detail relying on binary choices, modeled as the noisy accumulation of a decision variable to a threshold. We show that it breaks down when used to describe real-life human decision involving multiple options. We show instead that including obstacles in the diffusion model (a natural conceptual extension) can describe the data with great degree of accuracy. We evaluate this new model by capitalizing on the advent of big data, analyzing a vast corpus of decision making obtained from on-line chess servers. The present manuscript resolves a conflict between current theories of decision-making and concrete data, it solves this data with a concrete theoretical proposal and analyzes specific predictions of the model.

"Big data" in economic history

Big data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data - population and environment - discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.

The politics of data friction

Purpose

The purpose of this paper is twofold: first, to further develop Paul Edwards’ concept of “data friction” by examining the socio-material forces that are shaping data movements in the cases of research data and online communications data, second, to articulate a politics of data friction, identifying the interrelated infrastructural, socio-cultural and regulatory dynamics of data friction, and how these are contributing to the constitution of social relations.

Design/methodology/approach

The paper develops a hermeneutic review of the literature on socio-material factors influencing the movement of digital data between social actors in the cases of research data sharing and online communications data. Parallels between the two cases are identified and used to further develop understanding of the politics of “data friction” beyond the concept’s current usage within the Science Studies literature.

Findings

A number of overarching parallels are identified relating to the ways in which new data flows and the frictions that shape them bring social actors into new forms of relation with one another, the platformisation of infrastructures for data circulation, and state action to influence the dynamics of data movement. Moments and sites of “data friction” are identified as deeply political – resulting from the collective decisions of human actors who experience significantly different levels of empowerment with regard to shaping the overall outcome.

Research limitations/implications

The paper further develops Paul Edwards’ concept of “data friction” beyond its current application in Science Studies. Analysis of the broader dynamics of data friction across different cases identifies a number of parallels that require further empirical examination and theorisation.

Practical implications

The observation that sites of data friction are deeply political has significant implications for all engaged in the practice and management of digital data production, circulation and use.

Social implications

It is argued that the concept of “data friction” can help social actors identify, examine and act upon some of the complex socio-material dynamics shaping emergent data movements across a variety of domains, and inform deliberation at all levels – from everyday practice to international regulation – about how such frictions can be collectively shaped towards the creation of more equitable and just societies.

Originality/value

The paper makes an original contribution to the literature on friction in the dynamics of digital data movement, arguing that in many cases data friction may be something to enable and foster, rather than overcome. It also brings together literature from diverse disciplinary fields to examine these frictional dynamics within two cases that have not previously been examined in relation to one another.

THE RELATIONSHIPS BETWEEN LEADERSHIP, WORK ENGAGEMENT AND EMPLOYEE INNOVATIVE PERFORMANCE: EMPIRICAL EVIDENCE FROM THE INDIAN R&D CONTEXT

Integrating the behavioural theory of leadership with job demands–resources theory of engagement, the present study examines the process through which leadership impact R&D professionals’ innovative work behaviours and innovative performance (measured through peer-reviewed journal papers, patents, PhDs guided and keynote addresses delivered). Data from 467 scientists working in India’s largest civilian R&D organisation were collected and analysed using structural equation modelling. The study found that work engagement was positively related to innovative work behaviours as well as innovative performance. Leader behaviours had significant indirect effects on innovative work behaviours as well as innovative performance via work engagement. While the total effect of leadership on innovative work behaviours, the total effect was non-significant for innovative performance. Implications for theory and practice are discussed.

Univariate comparisons given aggregated normative data

OBJECTIVE

Normative comparison is a method to compare an individual to a norm group. It is commonly used in neuropsychological assessment to determine if a patient's cognitive capacities deviate from those of a healthy population. Neuropsychological assessment often involves multiple testing, which might increase the familywise error rate (FWER). Recently, several correction methods have been proposed to reduce the FWER. However these methods require that multivariate normative data are available, which is often not the case. We propose to obtain these data by merging the control group data of existing studies into an aggregated database. In this paper, we study how the correction methods fare given such an aggregated normative database.

METHODS

In a simulation study mimicking the aggregated database situation, we compared applying no correction, the Bonferroni correction, a maximum distribution approach and a stepwise approach on their FWER and their power to detect genuine deviations.

RESULTS

If the aggregated database contained data on all neuropsychological tests, the stepwise approach outperformed the other methods with respect to the FWER and power. However, if data were missing, the Bonferroni correction produced the lowest FWER.

DISCUSSION

Overall, the stepwise approach appears to be the most suitable normative comparison method for use in neuropsychological assessment. When the norm data contained large amounts of missing data, the Bonferroni correction proved best. Advice of which method to use in different situations is provided.

Conceptions of Good Science in Our Data-Rich World

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.

Spatio-temporal filtering techniques for the detection of disaster-related communication

Individuals predominantly exchange information with one another through informal, interpersonal channels. During disasters and other disrupted settings, information spread through informal channels regularly outpaces official information provided by public officials and the press. Social scientists have long examined this kind of informal communication in the rumoring literature, but studying rumoring in disrupted settings has posed numerous methodological challenges. Measuring features of informal communication-timing, content, location-with any degree of precision has historically been extremely challenging in small studies and infeasible at large scales. We address this challenge by using online, informal communication from a popular microblogging website and for which we have precise spatial and temporal metadata. While the online environment provides a new means for observing rumoring, the abundance of data poses challenges for parsing hazard-related rumoring from countless other topics in numerous streams of communication. Rumoring about disaster events is typically temporally and spatially constrained to places where that event is salient. Accordingly, we use spatio and temporal subsampling to increase the resolution of our detection techniques. By filtering out data from known sources of error (per rumor theories), we greatly enhance the signal of disaster-related rumoring activity. We use these spatio-temporal filtering techniques to detect rumoring during a variety of disaster events, from high-casualty events in major population centers to minimally destructive events in remote areas. We consistently find three phases of response: anticipatory excitation where warnings and alerts are issued ahead of an event, primary excitation in and around the impacted area, and secondary excitation which frequently brings a convergence of attention from distant locales onto locations impacted by the event. Our results demonstrate the promise of spatio-temporal filtering techniques for "tuning" measurement of hazard-related rumoring to enable observation of rumoring at scales that have long been infeasible.

Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians

Human mobility is becoming an accessible field of study, thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modelling framework based on Langevin dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.

Realising the technological promise of smartphones in addiction research and treatment: An ethical review

BACKGROUND

Smartphone technologies and mHealth applications (or apps) promise unprecedented scope for data collection, treatment intervention, and relapse prevention when used in the field of substance abuse and addiction. This potential also raises new ethical challenges that researchers, clinicians, and software developers must address.

AIMS

This paper aims to identify ethical issues in the current uses of smartphones in addiction research and treatment.

METHODS

A search of three databases (PubMed, Web of Science and PsycInfo) identified 33 studies involving smartphones or mHealth applications for use in the research and treatment of substance abuse and addiction. A content analysis was conducted to identify how smartphones are being used in these fields and to highlight the ethical issues raised by these studies.

RESULTS

Smartphones are being used to collect large amounts of sensitive information, including personal information, geo-location, physiological activity, self-reports of mood and cravings, and the consumption of illicit drugs, alcohol and nicotine. Given that detailed information is being collected about potentially illegal behaviour, we identified the following ethical considerations: protecting user privacy, maximising equity in access, ensuring informed consent, providing participants with adequate clinical resources, communicating clinically relevant results to individuals, and the urgent need to demonstrate evidence of safety and efficacy of the technologies.

CONCLUSIONS

mHealth technology offers the possibility to collect large amounts of valuable personal information that may enhance research and treatment of substance abuse and addiction. To realise this potential researchers, clinicians and app-developers must address these ethical concerns to maximise the benefits and minimise risks of harm to users.

Quantifying the link between art and property prices in urban neighbourhoods

Is there an association between art and changes in the economic conditions of urban neighbourhoods? While the popular media and policymakers commonly believe this to be the case, quantitative evidence remains lacking. Here, we use metadata of geotagged photographs uploaded to the popular image-sharing platform Flickr to quantify the presence of art in London neighbourhoods. We estimate the presence of art in neighbourhoods by determining the proportion of Flickr photographs which have the word 'art' attached. We compare this with the relative gain in residential property prices for each Inner London neighbourhood. We find that neighbourhoods which have a higher proportion of 'art' photographs also have greater relative gains in property prices. Our findings demonstrate how online data can be used to quantify aspects of the visual environment at scale and reveal new connections between the visual environment and crucial socio-economic measurements.