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Background: Peer evaluation is the cornerstone of science evaluation. In this paper, we analyze whether or not a form of peer evaluation, the pre-publication selection of the best papers in Computer Science (CS) conferences, is better than random, when considering future citations received by the papers. Methods: Considering 12 conferences (for several years), we collected the citation counts from Scopus for both the best papers and the non-best papers. For a different set of 17 conferences, we collected the data from Google Scholar. For each data set, we computed the proportion of cases whereby the best paper has more citations. We also compare this proportion for years before 2010 and after to evaluate if there is a propaganda effect. Finally, we count the proportion of best papers that are in the top 10% and 20% most cited for each conference instance. Results: The probability that a best paper will receive more citations than a non best paper is 0.72 (95% CI = 0.66, 0.77) for the Scopus data, and 0.78 (95% CI = 0.74, 0.81) for the Scholar data. There are no significant changes in the probabilities for different years. Also, 51% of the best papers are among the top 10% most cited papers in each conference/year, and 64% of them are among the top 20% most cited. Discussion: There is strong evidence that the selection of best papers in Computer Science conferences is better than a random selection, and that a significant number of the best papers are among the top cited papers in the conference. [ABSTRACT FROM AUTHOR]

On-site surveys involving face-to-face interviews are implemented globally across many scientific disciplines. Incorporating new technologies into such surveys by using electronic devices is becoming more common and is widely viewed to be more cost-effective and accurate. However, Electronic Data Capture methods (EDC) when compared to traditional Paper-based Data Capture (PDC) are often implemented without proper evaluation of any changes in efficiency, especially from surveys in coastal and marine environments. A roving creel survey of recreational shore-based fishers in Western Australia in 2019 enabled a direct comparison between the two methods. Randomisation strategies were employed to ensure biases in using each technique were minimised. A total of 1,068 interviews with recreational fishers were undertaken with a total error rate of 5.1% (CI95%: 4.8–5.3%) for PDC and 3.1% (CI95%: 2.9–3.3%) for EDC. These results confirmed that EDC can reduce errors whilst increasing efficiency and decreasing cost, although some aspects of this platform could be improved with some streamlining. This study demonstrates how EDC can be successfully implemented in coastal and marine environments without compromising the randomised, stratified nature of a survey and highlights the cost-effectiveness of this method. Such findings can be widely applied to any discipline which uses face-to-face interviews for data collection. [ABSTRACT FROM AUTHOR]

Before organizing mixed-mode data collection for the self-administered questionnaire of the Belgian Health Interview Survey, measurement effects between the paper-and-pencil and the web-based questionnaire were evaluated. A two-period cross-over study was organized with a sample of 149 employees of two Belgian research institutes (age range 22–62 years, 72% female). Measurement agreement was assessed for a diverse range of health indicators related to general health, mental and psychosocial health, health behaviors and prevention with kappa coefficients and intraclass correlation (ICC). The quality of the data collected by both modes was evaluated by quantifying the missing, ‘don’t know’ and inconsistent values and data entry mistakes. Good to very good agreement was found for all categorical indicators with kappa coefficients superior to 0.60, except for two mental and psychosocial health indicators namely the presence of a sleeping disorder and of a depressive disorder (kappa≥0.50). For the continuous indicators high to acceptable agreement was observed with ICC superior to 0.70. Inconsistent answers and data-entry mistakes were only occurring in the paper-and-pencil mode. There were no less missing values in the web-based mode compared to the paper-and-pencil mode. The study supports the idea that web-based modes provide, in general, equal responses to paper-and-pencil modes. However, health indicators based upon factual and objective items tend to have higher measurement agreement than indicators requiring an assessment of personal subjective feelings. A web-based mode greatly facilitates the data-entry process and guides the completing of a questionnaire. However, item non-response was not positively affected. [ABSTRACT FROM AUTHOR]

This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, traditional PTC data collection methods, mainly manual and paper-based, are time-consuming and often fail to capture the full complexity of care pathways. TimelinePTC addresses these limitations by providing a digital platform for collaborative, real-time data entry and visualization, thereby enhancing data accuracy and collection efficiency. Initially created for the Specialized Treatment Early in Psychosis (STEP) program in New Haven, Connecticut, its design allows for straightforward adaptation to other healthcare contexts, facilitated by its open-source codebase. The tool significantly simplifies the data collection process, making it more efficient and user-friendly. It automates the conversion of collected data into a format ready for analysis, reducing manual transcription errors and saving time. By enabling more detailed and consistent data collection, TimelinePTC has the potential to improve healthcare access research, supporting the development of targeted interventions to reduce DUP and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers. [ABSTRACT FROM AUTHOR]

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature. [ABSTRACT FROM AUTHOR]

The honesty and integrity of scientists is widely believed to be threatened by pressures to publish, unsupportive research environments, and other structural, sociological and psychological factors. Belief in the importance of these factors has inspired major policy initiatives, but evidence to support them is either non-existent or derived from self-reports and other sources that have known limitations. We used a retrospective study design to verify whether risk factors for scientific misconduct could predict the occurrence of retractions, which are usually the consequence of research misconduct, or corrections, which are honest rectifications of minor mistakes. Bibliographic and personal information were collected on all co-authors of papers that have been retracted or corrected in 2010-2011 (N=611 and N=2226 papers, respectively) and authors of control papers matched by journal and issue (N=1181 and N=4285 papers, respectively), and were analysed with conditional logistic regression. Results, which avoided several limitations of past studies and are robust to different sampling strategies, support the notion that scientific misconduct is more likely in countries that lack research integrity policies, in countries where individual publication performance is rewarded with cash, in cultures and situations were mutual criticism is hampered, and in the earliest phases of a researcher’s career. The hypothesis that males might be prone to scientific misconduct was not supported, and the widespread belief that pressures to publish are a major driver of misconduct was largely contradicted: high-impact and productive researchers, and those working in countries in which pressures to publish are believed to be higher, are less-likely to produce retracted papers, and more likely to correct them. Efforts to reduce and prevent misconduct, therefore, might be most effective if focused on promoting research integrity policies, improving mentoring and training, and encouraging transparent communication amongst researchers. [ABSTRACT FROM AUTHOR]

The gender gap in computer science (CS) research is a well-studied problem, with an estimated ratio of 15%–30% women researchers. However, far less is known about gender representation in specific fields within CS. Here, we investigate the gender gap in one large field, computer systems. To this end, we collected data from 72 leading peer-reviewed CS conferences, totalling 6,949 accepted papers and 19,829 unique authors (2,946 women, 16,307 men, the rest unknown). We combined these data with external demographic and bibliometric data to evaluate the ratio of women authors and the factors that might affect this ratio. Our main findings are that women represent only about 10% of systems researchers, and that this ratio is not associated with various conference factors such as size, prestige, double-blind reviewing, and inclusivity policies. Author research experience also does not significantly affect this ratio, although author country and work sector do. The 10% ratio of women authors is significantly lower than the 16% in the rest of CS. Our findings suggest that focusing on inclusivity policies alone cannot address this large gap. Increasing women's participation in systems research will require addressing the systemic causes of their exclusion, which are even more pronounced in systems than in the rest of CS. [ABSTRACT FROM AUTHOR]

Background: Person-centred healthcare focuses on placing the beliefs and values of service users at the centre of decision-making and creating the context for practitioners to do this effectively. Measuring the outcomes arising from person-centred practices is complex and challenging and often adopts multiple perspectives and approaches. Few measurement frameworks are grounded in an explicit person-centred theoretical framework. Aims: In the study reported in this paper, the aim was to develop a valid and reliable instrument to measure the experience of person-centred care by service users (patients)–The Person-centred Practice Inventory-Care (PCPI-C). Methods: Based on the 'person-centred processes' construct of an established Person-centred Practice Framework (PCPF), a service user instrument was developed to complement existing instruments informed by the same theoretical framework–the PCPF. An exploratory sequential mixed methods design was used to construct and test the instrument, working with international partners and service users in Scotland, Northern Ireland, Australia and Austria. A three-phase approach was adopted to the development and testing of the PCPI-C: Phase 1 –Item Selection: following an iterative process a list of 20 items were agreed upon by the research team for use in phase 2 of the project; Phase 2 –Instrument Development and Refinement: Development of the PCPI-C was undertaken through two stages. Stage 1 involved three sequential rounds of data collection using focus groups in Scotland, Australia and Northern Ireland; Stage 2 involved distributing the instrument to members of a global community of practice for person-centred practice for review and feedback, as well as refinement and translation through one: one interviews in Austria. Phase 3: Testing Structural Validity of the PCPI-C: A sample of 452 participants participated in this phase of the study. Service users participating in existing cancer research in the UK, Malta, Poland and Portugal, as well as care homes research in Austria completed the draft PCPI-C. Data were collected over a 14month period (January 2021-March 2022). Descriptive and measures of dispersion statistics were generated for all items to help inform subsequent analysis. Confirmatory factor analysis was conducted using maximum likelihood robust extraction testing of the 5-factor model of the PCPI-C. Results: The testing of the PCPI-C resulted in a final 18 item instrument. The results demonstrate that the PCPI-C is a psychometrically sound instrument, supporting a five-factor model that examines the service user's perspective of what constitutes person-centred care. Conclusion and implications: This new instrument is generic in nature and so can be used to evaluate how person-centredness is perceived by service users in different healthcare contexts and at different levels of an organisation. Thus, it brings a service user perspective to an organisation-wide evaluation framework. [ABSTRACT FROM AUTHOR]

While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by active and inactive alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. The currently common approaches have three major drawbacks. First, although it is a long tradition in genetics, modeling the effect of an inactive allele (a null mutant allele) contrasted against that of the active allele (the wild-type allele) is not suitable for mechanistic understanding of multi-gene systems. Second, a commonly-used additive model (ANOVA with interaction) mathematically fails in estimation of interactions among more than two genes when the phenotypic response is not linear. Third, interpretation of higher-order interactions defined by an additive model is not intuitive. I derived an averaging model based on algebraic principles to solve all these problems within the framework of a general linear model. In the averaging model: the effect of the active allele is contrasted against the effect of the inactive allele for easier mechanistic interpretations; there is mathematical stability in estimation of higher-order interactions even when the phenotypic response is not linear; and interpretations of higher-order interactions are intuitive and consistent—interactions are defined as the mean effects of the last active genes added to the system. Thus, the key outcomes of this study are development of the averaging model, which is suitable for analysis of multi-gene systems, and a new, intuitive, and mathematically and interpretationally consistent definition of a genetic interaction, which is central to the averaging model. [ABSTRACT FROM AUTHOR]

Tablet-adapted measures provide an efficient, accurate method of data collection for large-scale studies. The Castles and Coltheart Reading Test 2 (CC2) is a standardized paper-and-pencil measure of children's reading ability. In the current study, the CC2 was administered to 603 children aged 7–8 years via iPad using electronic data capture software. Results indicate the tablet-adapted measure could be reliably administered by non-clinical staff and showed quantitative equivalence, i.e., comparable score distributions, to CC2 normative data. Internal consistency was good for regular and non-word lists. Findings suggest that the tablet-adapted CC2 is a viable tool for large research studies. [ABSTRACT FROM AUTHOR]

Intelligentization-oriented development is a fast-developing trend of technological revolution. It promotes the reconstruction of the industrial system of a region and affects its overall industrial competitiveness. This paper sets up a variety of models featuring intelligentization level and multi-dimensional industrial competitiveness, and collects data of 28 provinces and cities in China from 2003 to 2017 to test the influence of industrial intelligentization level on the industrial competitiveness of a region. The result reveals that: 1) In China's provincial jurisdictions, the higher the level of intelligentization is, the lower the overall level of industrial competitiveness and the lower the proportion of industry in the economic system will be. In regions where the facilities are highly intelligentialized, the production sectors tend to move to the less developed regions, and the growth effect of technological dividends is the focus. 2) Compared with the middle region and the Western region of China, the Eastern region, which is more developed with higher intelligentization level, has stronger ability in the research and development (R&D) of technologies, and the economic structure of the industry there tends to be stable, manifesting a strong growth potential. [ABSTRACT FROM AUTHOR]

Background: Large cross-sectional household surveys are common for measuring indicators of neglected tropical disease control programs. As an alternative to standard paper-based data collection, we utilized novel paperless technology to collect data electronically from over 12,000 households in Ethiopia. Methodology: We conducted a needs assessment to design an Android-based electronic data collection and management system. We then evaluated the system by reporting results of a pilot trial and from comparisons of two, large-scale surveys; one with traditional paper questionnaires and the other with tablet computers, including accuracy, person-time days, and costs incurred. Principle Findings: The electronic data collection system met core functions in household surveys and overcame constraints identified in the needs assessment. Pilot data recorders took 264 (standard deviation (SD) 152 sec) and 260 sec (SD 122 sec) per person registered to complete household surveys using paper and tablets, respectively (P = 0.77). Data recorders felt a lack of connection with the interviewee during the first days using electronic devices, but preferred to collect data electronically in future surveys. Electronic data collection saved time by giving results immediately, obviating the need for double data entry and cross-correcting. The proportion of identified data entry errors in disease classification did not differ between the two data collection methods. Geographic coordinates collected using the tablets were more accurate than coordinates transcribed on a paper form. Costs of the equipment required for electronic data collection was approximately the same cost incurred for data entry of questionnaires, whereas repeated use of the electronic equipment may increase cost savings. Conclusions/Significance: Conducting a needs assessment and pilot testing allowed the design to specifically match the functionality required for surveys. Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic led to a severe economic shock in the United States. This paper uses a unique survey data collected early on in the pandemic to measure economic deprivation among individuals. The Federal Reserve Board fielded a Survey of Household Economics and Decision-making (SHED) in April 2020. This survey is used to compile data on four indicators of economic deprivation, namely: i) Overall financial condition, ii) Loss of employment, iii) Reduction in income and iv) Inability to pay bills in full. Data on these indicators is compiled for each individual and is used in a novel way to construct a set of multidimensional deprivation indices. These indices measure the overlap of deprivations experienced by an individual. Results show that almost 25 percent of the respondents faced hardships in at least two of the four indicators. More than 13 percent of adults reported their inability to pay monthly bills and struggled to make ends meet financially. One in four respondents had lower income compared to income from previous month. The economic shock affected Hispanics in a more profound way. More than 37 percent Hispanics reported hardship in two or more indicators and 8 percent reported hardship in all four indicators. Higher proportion of young adults and those without a college degree suffered multiple hardships. The paper highlights the plight of Americans during the early months of the economic crisis set in motion amid the coronavirus pandemic and sheds light on how economic disparities deepened along racial/ethnic lines. [ABSTRACT FROM AUTHOR]

Background: To be meaningful, a core outcome set (COS) should be relevant to all stakeholders including patients and carers. This review aimed to explore the methods by which patients and carers have been included as participants in COS development exercises and, in particular, the use and reporting of qualitative methods. Methods: In August 2015, a search of the Core Outcomes Measures in Effectiveness Trials (COMET) database was undertaken to identify papers involving patients and carers in COS development. Data were extracted to identify the data collection methods used in COS development, the number of health professionals, patients and carers participating in these, and the reported details of qualitative research undertaken. Results: Fifty-nine papers reporting patient and carer participation were included in the review, ten of which reported using qualitative methods. Although patients and carers participated in outcome elicitation for inclusion in COS processes, health professionals tended to dominate the prioritisation exercises. Of the ten qualitative papers, only three were reported as a clear pre-designed part of a COS process. Qualitative data were collected using interviews, focus groups or a combination of these. None of the qualitative papers reported an underpinning methodological framework and details regarding data saturation, reflexivity and resource use associated with data collection were often poorly reported. Five papers reported difficulty in achieving a diverse sample of participants and two reported that a large and varied range of outcomes were often identified by participants making subsequent rating and ranking difficult. Conclusions: Consideration of the best way to include patients and carers throughout the COS development process is needed. Additionally, further work is required to assess the potential role of qualitative methods in COS, to explore the knowledge produced by different qualitative data collection methods, and to evaluate the time and resources required to incorporate qualitative methods into COS development. [ABSTRACT FROM AUTHOR]

This paper aims to identify the determinants of the length of stay (LoS) of international tourists in Norway. The paper reassesses the standard assumption related to tourists' LoS; it refers to the travel industry's current trends, and it postulates a more sustainable approach to analyzing tourists' LoS at the destination level. The paper concludes with a series of recommendations. The data for this study were collected during 153 data collection days and among 5,300 travelers in Norway. The determinants of LoS were analyzed by means of an ordinary least squares (OLS) regression. The results indicate that tourists' LoS is positively related to their age, interests (nature-based tourists), origin (German, Dutch tourists) and mode of travel organization (package tourists). A negative and significant effect on tourists' LoS was found for tourists' interests (urban-based tourists), spending, and origin (home market, long-haul tourists). No significant results were revealed for two covariates, namely, gender and repeat visitation. [ABSTRACT FROM AUTHOR]

The Consolidated Framework for Implementation Research (CFIR) is a well-established framework for systematically identifying key factors influencing the implementation of programs. To enhance the classification of existing CFIR definitions, as well as its +2/-2 scoring system, this study incorporated the views of relevant experts to: i) improve how themes are scored; and ii) utilise more information regarding the frequency with which themes are identified. This structured, frequency-based approach to the CFIR's scoring process has been trialled as the CFIR-frequency (CFIR-f). Researchers thematically analysed semi-structured interview data from four groups of policy and program experts (N = 24) delivering two family-based therapies in New South Wales (NSW), Australia. Themes identified by less than 50% were excluded from further analysis. Themes identified by 50% or more of expert participants in the four groups were classified as enablers or barriers using clearly defined criteria. Each theme was allocated a score according to how many experts identified it as an enabler or barrier, and then mapped back onto the latest adaptation of the CFIR comprising 67 constructs. The CFIR-f successfully determined three enablers of, and six barriers to, implementation. Enablers included the family-based therapy programs, therapist training and participant monitoring systems. Barriers included referral, data collection and staffing difficulties, NSW adaptation issues and the suitability and safety of the programs for Aboriginal families. The same enablers and barriers were identified using both the CFIR-f and the original scoring approach, and the identified themes were successfully mapped to almost all CFIR constructs (65/67). This paper proposes a more frequency-based approach to CFIR's scoring process (the CFIR-f). By specifically utilising the frequency with which these barriers and enablers are identified, the CFIR-f engenders a list of ranked themes that service providers and policymakers can use to inform their decisions about program modification and implementation. [ABSTRACT FROM AUTHOR]

Empowered by advanced on-board sensors, high-performance optics packages and ever-increasing computational power, smartphones have democratized data generation, collection, and analysis. Building on this capacity, many platforms have been developed to enable its use as an optical sensing platform for colorimetric and fluorescence measurements. In this paper, we report the ability to enable a smartphone to perform laboratory quality time-resolved analysis of luminescent samples via the exploitation of the rolling shutter mechanism of the native CMOS imager. We achieve this by leveraging the smartphone's standard image capture applications, commercially available image analysis software, and housing the device within a UV-LED containing case. These low-cost modifications enable us to demonstrate the smartphone's analytical potential by performing tasks ranging from authentication and encryption to the interrogation of packaging, compounds, and physical phenomena. This approach underscores the power of repurposing existing technologies to extend the reach and inclusivity of scientific exploration, opening new avenues for data collection and analysis. [ABSTRACT FROM AUTHOR]

Dispersal knowledge is essential for conservation management, and demand is growing. But are we accumulating dispersal knowledge at a pace that can meet the demand? To answer this question we tested for changes in dispersal data collection and use over time. Our systematic review of 655 conservation-related publications compared five topics: climate change, habitat restoration, population viability analysis, land planning (systematic conservation planning) and invasive species. We analysed temporal changes in the: (i) questions asked by dispersal-related research; (ii) methods used to study dispersal; (iii) the quality of dispersal data; (iv) extent that dispersal knowledge is lacking, and; (v) likely consequences of limited dispersal knowledge. Research questions have changed little over time; the same problems examined in the 1990s are still being addressed. The most common methods used to study dispersal were occupancy data, expert opinion and modelling, which often provided indirect, low quality information about dispersal. Although use of genetics for estimating dispersal has increased, new ecological and genetic methods for measuring dispersal are not yet widely adopted. Almost half of the papers identified knowledge gaps related to dispersal. Limited dispersal knowledge often made it impossible to discover ecological processes or compromised conservation outcomes. The quality of dispersal data used in climate change research has increased since the 1990s. In comparison, restoration ecology inadequately addresses large-scale process, whilst the gap between knowledge accumulation and growth in applications may be increasing in land planning. To overcome apparent stagnation in collection and use of dispersal knowledge, researchers need to: (i) improve the quality of available data using new approaches; (ii) understand the complementarities of different methods and; (iii) define the value of different kinds of dispersal information for supporting management decisions. Ambitious, multi-disciplinary research programs studying many species are critical for advancing dispersal research. [ABSTRACT FROM AUTHOR]

The funding statement for this paper is incorrect. The correct funding statement is: This study was supported by the Thammasat Postdoctoral Fellowship (grant number TUPD7/2564; https://research.tu.ac.th/tu-research-funds) and the Thailand Science Research and Innovation Fundamental Fund for the fiscal year 2023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.By Teerawat Simmachan; Noppachai Wongsai; Sangdao Wongsai and Rattana LerdsuwansriReported by Author; Author; Author; Author [Extracted from the article]

This correction notice addresses several errors and omissions in a research paper titled "Prevalence of major depressive disorder and its determinants among young married women and unmarried girls: Findings from the second round of UDAYA survey." The funding statement has been corrected to acknowledge the support of the Bill & Melinda Gates Foundation and the David and Lucile Packard Foundation. The Acknowledgments section has been updated to include the financial support of the Bill & Melinda Gates Foundation for the publication of the research. Additionally, there is a correction to the affiliation of author Joyeeta Thakur, and the ORCID iDs for several authors have been provided. [Extracted from the article]

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology. [ABSTRACT FROM AUTHOR]

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique. [ABSTRACT FROM AUTHOR]

Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners' performance within and between countries. [ABSTRACT FROM AUTHOR]

Since the financial system has illustrated an increasingly prominent characteristic of inextricable connections, information theory is gradually utilized to study the financial system. By collecting the daily data of industry index (2005-2020) and region index (2012-2020) listed in China as samples, this paper applies an innovative measure named partial mutual information on mixed embedding to generate directed networks. Based on the analysis of nonlinear relationships among sectors, this paper realizes the accurate construction of "time-varying" financial network from the perspective of risk spillover. The results are presented as follow: (1) interactions can be better understood through the nonlinear networks among distinct sectors, and sectors in the networks could be classified into different types according to their topological properties connected to risk spillover; (2) in the rising stage, information is transmitted rapidly in the network, so the risk is fast diffused and absorbed; (3) in the declining stage, the network topology is more complex and panic sentiments have long term impact leading to more connections; (4) The US market, Japan market and Hongkong market have significant affect on China's market. The results suggest that this nonlinear measure is an effective approach to develop financial networks and explore the mechanism of risk spillover. [ABSTRACT FROM AUTHOR]

Background: Two accepted designs exist for parallel-group cluster-randomised trials (CRTs). Closed-cohort designs follow the same individuals over time with a single recruitment period before randomisation, but face challenges in settings with high attrition. (Repeated) cross-sectional designs recruit at one or more timepoints before and/or after randomisation, collecting data from different individuals present in the cluster at these timepoints, but are unsuitable for assessment of individual change over time. An 'open-cohort' design allows individual follow-up with recruitment before and after cluster-randomisation, but little literature exists on acceptability to inform their use in CRTs. Aim: To document the views and experiences of expert trialists to identify: a) Design and conduct challenges with established parallel-group CRT designs, b) Perceptions of potential benefits and barriers to implementation of open-cohort CRTs, c) Methods for minimising, and investigating the impact of, bias in open-cohort CRTs. Methods: Qualitative consultation via two expert workshops including triallists (n = 24) who had worked on CRTs over a range of settings. Workshop transcripts were analysed using Descriptive Thematic Analysis utilising inductive and deductive coding. Results: Two central organising concepts were developed. Design and conduct challenges with established CRT designs confirmed that current CRT designs are unable to deal with many of the complex research and intervention circumstances found in some trial settings (e.g. care homes). Perceptions of potential benefits and barriers of open cohort designs included themes on: approaches to recruitment; data collection; analysis; minimising/investigating the impact of bias; and how open-cohort designs might address or present CRT design challenges. Open-cohort designs were felt to provide a solution for some of the challenges current CRT designs present in some settings. Conclusions: Open-cohort CRT designs hold promise for addressing the challenges associated with standard CRT designs. Research is needed to provide clarity around definition and guidance on application. [ABSTRACT FROM AUTHOR]

Introduction: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. Methods: The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches–an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. Results: The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). Discussion: Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection. [ABSTRACT FROM AUTHOR]

Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict and recommend next user locations based on the current position. Thus, in this paper, we explore the computation of the transition patterns, especially with a trajectory dataset collected using differential privacy, which is a de facto standard for privacy-preserving data collection and processing. Specifically, the proposed scheme relies on geo-indistinguishability, which is a variant of the well-known differential privacy, to collect trajectory data from users in a privacy-preserving manner, and exploits the functionality of the expectation-maximization algorithm to precisely estimate hidden transition patterns based on perturbed trajectory datasets collected under geo-indistinguishability. Experimental results using real trajectory datasets confirm that a good estimation of transition pattern can be achieved with the proposed method. [ABSTRACT FROM AUTHOR]

Several technical indicators have been proposed to assess the impact of authors and institutions. Here, we combine the h-index and the PageRank algorithm to do away with some of the individual limitations of these two indices. Most importantly, we aim to take into account value differences between citations-evaluating the citation sources by defining the h-index using the PageRank score rather than with citations. The resulting PR-index is then constructed by evaluating source popularity as well as the source publication authority. Extensive tests on available collections data (i.e., Microsoft Academic Search and benchmarks on the SIGKDD innovation award) show that the PR-index provides a more balanced impact measure than many existing indices. Due to its simplicity and similarity to the popular h-index, the PR-index may thus become a welcome addition to the technical indices already in use. Moreover, growth dynamics prior to the SIGKDD innovation award indicate that the PR-index might have notable predictive power. [ABSTRACT FROM AUTHOR]

Backward-moving kinematic waves (KWs) (e.g., stop-and-go traffic conditions and a shock wave) cause unsafe driving conditions, decreases in the capacities of freeways, and increased travel time. In this paper, a sequential filtering method is proposed to detect KWs using data collected in a connected environment, which can aid in developing a traffic control strategy for connected vehicles to stop or dampen the propagation of these KWs. The proposed method filters out random fluctuation in the data using ensemble empirical mode decomposition that considers the spectral features of KWs. Then, the spatial movements of KWs are considered using cross-correlation to identify potential candidate KWs. Asynchronous changes in the denoised flow and speed are used to evaluate candidate KWs using logistic regression to identify the KWs from localized reductions in speed that are not propagated upstream. The findings from an empirical evaluation of the proposed method showed strong promise for detecting KWs using data in a connected environment, even at 30% of the market penetration rates. This paper also addresses how data resolution of the connected environment affects the performance in detecting KWs. [ABSTRACT FROM AUTHOR]

Producing statistically robust profiles of small or 'hard-to-reach' populations has always been a challenge for researchers. Since surveying the wider population in order to capture a large enough sample of cases is usually too costly or impractical, researchers have been opting for 'snowballing' or 'time-location sampling'. The former does not allow for claims to representativeness, and the latter struggles with under-coverage and estimating confidence intervals. Respondent Driven Sampling (RDS) is a method that combines snowballing sampling with an analytical algorithm that corrects for biases that arise in snowballing. For all its advantages, a major weakness of RDS has been around data collection. Traditionally done on-site, the process is costly and lengthy. When done online, it is cheaper and faster but under a serious threat from fraud, compromising data quality and validity of findings. This paper describes a real-life application of a RDS data collection system that maximizes fraud prevention while still benefiting from low cost and speedy data collection. [ABSTRACT FROM AUTHOR]

Objective: Data collection in the field of acquired brain injury (ABI) lacks uniformity due to the broad spectrum of available measurement instruments, leading to incomparability of data and the need for patients to 'repeat their story'. To pursue uniform data collection, an ABI-specific minimal dataset (MDS-ABI) is currently under development. The current study aimed to assess the feasibility (performance according to protocol, user opinion, potential implementation barriers, and suggested improvements) of the MDS-ABI in clinical settings. Methods: A mixed-methods approach was used in a range of healthcare sectors for persons with ABI. Clinicians of several relevant disciplines within these sectors were asked to administer the MDS-ABI to five patients. Subsequently, feasibility according to clinicians was assessed by means of a paper questionnaire about every administration and an online questionnaire about the feasibility in general. Feasibility according to patients was assessed with a paper questionnaire and think aloud interviews. Results: Thirteen clinicians and 50 patients were included. In general, the MDS-ABI performed according to protocol. Both clinicians and patients were overall satisfied with the content of the MDS-ABI. The Cumulative Illness Rating Scale was regarded incomprehensible, leading to missing data. Further, clinicians indicated that the MDS-ABI would not be suitable for all ABI-patients, as some are incapable of self-report due to potential cognitive problems, communicative problems, fatigue, perceptual problems, or impaired awareness of deficits. Conclusion: The MDS-ABI is a promising tool for obtaining core information on ABI-patients. The MDS-ABI will be adjusted according to the suggestions. For patients who are incapable of self-report, a proxy-reported version of the self-reported part was developed. [ABSTRACT FROM AUTHOR]

Many crashes occur around freeway exit ramp areas in China due to excessive speeds and large speed variances. Traditionally, a single posted ramp speed limit sign is installed around the physical gore area to manage the speed. To address this issue, the study presented in this paper proposes the use of an advisory exit speed sign (AESS), which is an additional exit speed limit sign positioned along the deceleration lane to accommodate the speed changes ahead of the physical gore. The study selected three sites with similar exit ramp configurations and two scenarios (with AESS/without AESS) to quantify the influences of the AESS on the speed of exiting vehicles. The speed profiles of 480 vehicles were obtained based on 12 hours of data collection. A t-test was applied to verify the reduction in mean speed between the two scenarios. The results show that the AESS in this study was effective in reducing the mean speed and 85th percentile speed, especially in the taper and deceleration lane. It was clearly seen that drivers began to decelerate in advance when the AESS was installed, which led to a smooth deceleration process, especially on the segment between the theoretical gore and the physical gore. The AESS was also helpful in reducing speeding to some extent. Although the effects of the AESS on speed reduction at curved ramps were not ideal, the speed fluctuation range tended to be more contracted when the AESS was installed. This paper provides useful information for researchers, managers, and engineers when considering the implementation of AESS. [ABSTRACT FROM AUTHOR]

The sustainable development of mycorrhizal industry is the key to solve the problem of "mycorrhizal forestry contradiction". As a major province of edible mushroom production and forestry resources in China, Fujian Province is also an important origin of mycorrhizal technology research and development, so it is more typical and practical to establish an index system to evaluate the sustainability of mycorrhizal industry development in Fujian Province. Through research interviews and data collection, a sustainable capacity evaluation system of mycorrhizal industry was established with 21 indicators in six dimensions: economic, ecological, social, cultural, political, and technological. A combination of CRITIC empowerment method and cloud model was used to evaluate the sustainability of mycorrhizal industry development in Fujian Province. The results show that although the economic sustainability of the mycorrhizal industry in Fujian Province is average, the overall development trend is good and there are not too many problems. The sustainability of ecological, social and technological levels all have large differences in the development of indicators and the overall development status is average, but overall, the ecological, social and technological levels show a steady forward development from 2017 to 2020. The cultural and political dimensions of sustainability not only have large differences in the development of indicators and an average overall development status, but also have a small development span from 2017 to 2020 and a slow overall development. [ABSTRACT FROM AUTHOR]

Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational methodology unfolds across four strata: the data origin layer, data acquisition layer, data classification layer, and data optimization layer. Central to this endeavor are multi-objective optimal datasets (MooM), characterized by attribute-driven feature cartography and cluster categorization through the conduit of federated learning models. The orchestration of feature synchronization and parameter extraction transpires across multiple tiers of neural networking, culminating in the provisioning of a steadfast remedy through dataset standardization and labeling. The empirical findings reflect the efficacy of the proposed technique, boasting an impressive 97.34% accuracy rate in the disentanglement and clustering of telemedicine data, facilitated by the operational servers within the ambit of the federated model. [ABSTRACT FROM AUTHOR]

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels. [ABSTRACT FROM AUTHOR]

Background: Currently there is a critical need for accurate and standardized wildlife-vehicle collision data, because it is the underpinning of mitigation projects that protect both drivers and wildlife. Gathering data can be challenging because wildlife-vehicle collisions occur over broad areas, during all seasons of the year, and in large numbers. Collecting data of this magnitude requires an efficient data collection system. Presently there is no widely adopted system that is both efficient and accurate. Methodology/Principal Findings: Our objective was to develop and test an integrated smartphone-based system for reporting wildlife-vehicle collision data. The WVC Reporter system we developed consisted of a mobile web application for data collection, a database for centralized storage of data, and a desktop web application for viewing data. The smartphones that we tested for use with the application produced accurate locations (median error = 4.6–5.2 m), and reduced location error 99% versus reporting only the highway/marker. Additionally, mean times for data entry using the mobile web application (22.0–26.5 s) were substantially shorter than using the pen/paper method (52 s). We also found the pen/paper method had a data entry error rate of 10% and those errors were virtually eliminated using the mobile web application. During the first year of use, 6,822 animal carcasses were reported using WVC Reporter. The desktop web application improved access to WVC data and allowed users to easily visualize wildlife-vehicle collision patterns at multiple scales. Conclusions/Significance: The WVC Reporter integrated several modern technologies into a seamless method for collecting, managing, and using WVC data. As a result, the system increased efficiency in reporting, improved accuracy, and enhanced visualization of data. The development costs for the system were minor relative to the potential benefits of having spatially accurate and temporally current wildlife-vehicle collision data. [ABSTRACT FROM AUTHOR]

The real-time accurate measurement of the geomagnetic-field is the foundation to achieving high-precision geomagnetic navigation. The existing geomagnetic-field measurement models are essentially simplified models that cannot accurately describe the sources of measurement error. This paper, on the basis of systematically analyzing the source of geomagnetic-field measurement error, built a complete measurement model, into which the previously unconsidered geomagnetic daily variation field was introduced. This paper proposed an extended Kalman-filter based compensation method, which allows a large amount of measurement data to be used in estimating parameters to obtain the optimal solution in the sense of statistics. The experiment results showed that the compensated strength of the geomagnetic field remained close to the real value and the measurement error was basically controlled within 5nT. In addition, this compensation method has strong applicability due to its easy data collection and ability to remove the dependence on a high-precision measurement instrument. [ABSTRACT FROM AUTHOR]

Fishing fleets and targeted stocks are the basis for the design of multiannual management plans at European or Mediterranean levels. Management Strategy Evaluation and bioeconomic modeling need data at a specific level of resolution in terms of time, area and type of fishing activity for analyzing measures for management procedures using simulations. Within the Data Collection Framework, data are to be aggregated at different levels, e.g.: fleet segment and métier, the former linked to the predominant gear and the size of the vessel and the latter to the activity itself. Fishing costs are collected by fleet segment, effort and landings by fleet segment and métier. Bioeconomic modeling for management purposes requires data at the same resolution. The aim of this paper is to describe a methodology, implemented in SECFISH R package, to disaggregate variable cost data from the fleet segment to the métier level. The presented tool allows to determine the correlation between the variable costs of a vessel and its activities to estimate costs at the activity level (e.g. métiers). The tool is applied to selected Italian fleet segments characterized by a variety of métiers and high dynamicity. [ABSTRACT FROM AUTHOR]

Background: The concept 'vulnerability' is prevalent in the public domain, health care, social institutions and multidisciplinary research. Conceptual heterogeneity is present, hampering the creation of a common evidence-base of research achievements and successful policies. Recently an international expert group combined a specific literature review with a 2-stage Delphi procedure, arriving at a seemingly universal concept of vulnerability for the elderly with applications for research instruments. We replicated and extended this study, to generalize this result to health in general, and perinatal health in particular. Methods: Two independent expert panels (general health, perinatal health) repeated the Delphi-procedure, using an extended and updated literature review to derive statements on the concept and defining pathways of vulnerability. Additional views were collected on research tools. Consensus-by-design was explicitly avoided. Data collection and processing was independent. Results: Both panels showed surprising convergence on the pathways of vulnerability to health/ill-health, and their interaction. The agreed conceptual model describes a dynamic relation between health and ill-health and vulnerability. The 2 key pathways that link to vulnerability, are complementary, but not symmetrical as biological processes of maintaining health or obtaining better health are not reciprocal to recovery, so also not in terms of vulnerability impacts. An individual's degree of vulnerability is the net balance of risk effects and protective and healing factors (socially, biologically and in terms of health literacy and health care access). These factors can for measurement purposes (according to the panels: interview for exploration, checklists for population research) be grouped into ‘material resources’, ‘taking responsibility for one’s own health’, ‘risky activities and behaviors’, and ‘social support’. Supportive and transforming action can thus be undertaken. Conclusion: A universal concept of vulnerability in the context of health was successfully derived after careful replication and extension of an international Delphi study on vulnerability among the elderly. [ABSTRACT FROM AUTHOR]

Recently, as the paradigm of medical services has shifted from treatment to prevention, there is a growing interest in smart healthcare that can provide users with healthcare services anywhere, at any time, using information and communications technologies. With the development of the smart healthcare industry, there is a growing need for collecting large-scale personal health data to exploit the knowledge obtained through analyzing them for improving the smart healthcare services. Although such a considerable amount of health data can be a valuable asset to the smart healthcare fields, they may cause serious privacy problems if sensitive information of an individual user is leaked to outside users. Therefore, most individuals are reluctant to provide their health data to smart healthcare service providers for data analysis and utilization purpose, which is the biggest challenge in smart healthcare fields. Thus, in this paper, we develop a novel mechanism for privacy-preserving collection of personal health data streams that is characterized as temporal data collected at fixed intervals by leveraging local differential privacy (LDP). In particular, with the proposed approach, a data contributor uses a given privacy budget of LDP to report a small amount of salient data, which are extracted from an entire health data stream, to a data collector. Then, a data collector can effectively reconstruct a health data stream based on the noisy salient data received from a data contributor. Experimental results demonstrate that the proposed approach provides significant accuracy gains over straightforward solutions to this problem. [ABSTRACT FROM AUTHOR]

In the United States, increasing access to the internet, the increasing costs of large-scale face-to-face data collections, and the general reluctance of the public to participate in intrusive in-person data collections all mean that new approaches to nationally representative surveys are urgently needed. The COVID-19 pandemic accelerated the need for faster, higher-quality alternatives to face-to-face data collection. These trends place a high priority on the evaluation of innovative web-based data collection methods that are convenient for the U.S. public and yield scientific information of high quality. The web mode is particularly appealing because it is relatively inexpensive, it is logistically flexible to implement, and it affords a high level of privacy and confidentiality when correctly implemented. With this study, we aimed to conduct a methodological evaluation of a sequential mixed-mode web/mail data collection protocol, including modular survey design concepts, which was implemented on a national probability sample in the U.S. in 2020–2021. We implemented randomized experiments to test theoretically-informed hypotheses that 1) the use of mail and increased incentives to follow up with households that did not respond to an invitation to complete a household screening questionnaire online would help to recruit different types of households; and 2) the use of modular survey design, which involves splitting a lengthy self-administered survey up into multiple parts that can be completed at a respondent's convenience, would improve survey completion rates. We find support for the use of mail and increased incentives to follow up with households that have not responded to a web-based screening questionnaire. We did not find support for the use of modular design in this context. Simple descriptive analyses also suggest that attempted telephone reminders may be helpful for the main survey. [ABSTRACT FROM AUTHOR]

This paper uses newly available data from Web of Science on publications matched to researchers in Survey of Doctorate Recipients to compare the quality of scientific publication data collected by surveys versus algorithmic approaches. We illustrate the different types of measurement errors in self-reported and machine-generated data by estimating how publication measures from the two approaches are related to career outcomes (e.g., salaries and faculty rankings). We find that the potential biases in the self-reports are smaller relative to the algorithmic data. Moreover, the errors in the two approaches are quite intuitive: the measurement errors in algorithmic data are mainly due to the accuracy of matching, which primarily depends on the frequency of names and the data that was available to make matches, while the noise in self reports increases over the career as researchers' publication records become more complex, harder to recall, and less immediately relevant for career progress. At a methodological level, we show how the approaches can be evaluated using accepted statistical methods without gold standard data. We also provide guidance on how to use the new linked data. [ABSTRACT FROM AUTHOR]

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network. [ABSTRACT FROM AUTHOR]

Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress. [ABSTRACT FROM AUTHOR]

In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. [ABSTRACT FROM AUTHOR]

We advance bi-national link-tracing sampling design, an innovative data collection methodology for sampling from so-called "transnational social fields", i.e. transnational networks embedding migrants, returned migrants and non-migrants. This paper describes our contributions to this methodology and its empirical implementation, and evaluates the features of the resulting networks (sample), with the aim to guide future research. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (Dâmbovița) and in a migration receiving Spanish town (Castellón). Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the link-tracing nomination patterns are affected by sex and residence homophily. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the growing invention in the current development of different domains like industries, e-health, and education, etc. Semantic web of things (SWoT) is an extension of IoT that enhance the communication by behaving intelligently. SWoT comprises 7 layered architecture. The perception layer is an important layer for collecting data from devices and to communicate with its associated layer. The data loss at the perception layer is very common due to inadequate resources, unpredictable link, noise, collision, and unexpected damage. To address this problem, we propose a method based on Compressive Sensing which recovers and estimates sensory data from a low-rank structure. The contribution of this paper is three folds. Firstly, we determine the problem of data acquisition and data loss at semantic sensory nodes in SWoT. Secondly, we introduce a compressive sensing based framework for SWoT that recovers the data accurately using low-rank features. Thirdly, the data estimation method is utilized to reduce the volume of the data. Proposed Compressive Sensing based Data Recoverability and Estimation (CS-RE) method is evaluated and compared with the existing reconstruction methods. The simulation results on real sensory datasets depict that the proposed method significantly outperforms existing methods in terms of error ratio and data recoverability accuracy. [ABSTRACT FROM AUTHOR]