Formula 1 drivers urged the operators of the Interlagos circuit to improve the new surface they laid ahead of this year's event.

This post discusses an update to Search Console's user and permissions to improve the accuracy and reflect the actual state of unused ownership tokens.

Recently, IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX for short) has provided us with low-cost, high efficiency and high bandwidth network services. However, as with the WiFi, the radio wave transmission also makes the WiMAX face the wireless transmission security problem. To solve this problem, the IEEE802.16Std during its development stage defines the Privacy Key Management (PKM for short) authentication process which offers a one-way authentication. However, using a one-way authentication, an SS may connect to a fake BS. Mutual authentication, like that developed for PKMv2, can avoid this problem. Therefore, in this paper, we propose an authentication key management approach, called Diffie-Hellman-PKDS-based authentication method (DiHam for short), which employs a secret door asymmetric one-way function, Public Key Distribution System (PKDS for short), to improve current security level of facility authentication between WiMAX's BS and SS. We further integrate the PKMv1 and the DiHam into a system, called PKM-DiHam (P-DiHam for short), in which the PKMv1 acts as the authentication process, and the DiHam is responsible for key management and delivery. By transmitting securely protected and well-defined parameters for SS and BS, the two stations can mutually authenticate each other. Messages including those conveying user data and authentication parameters can be then more securely delivered.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically enhance traditional flipped methodologies in a first-year engineering course, by using low-cost technology aids and proven pedagogical techniques to enhance student learning. Implemented in a first-year engineering course, this modified flipped model demonstrated an improved student awareness of essential engineering concepts and improved academic performance through collaborative and active learning activities, including flipped learning methodologies, without the need for expensive, formal active learning spaces. These findings have been validated through two studies and have shown similar results confirming that student learning is improved by the implementation of multi-pedagogical strategies in-formed by the use of an instructional design in a traditional classroom setting.

Aim/Purpose: Improving public schools is a focus of federal legislation in the United States with much of the burden placed on principals. However, preparing principals for this task has proven elusive despite many changes in programming by institutions of higher learning. Emerging technologies that rely on augmented and virtual realities are posited to be powerful pedagogical tools for closing this gap. Background: This study investigated the effects of immersive simulation technologies on principals’ self-efficacy after treatment and the perceived significance of the design of the immersive simulation experience as an effective tool for adult learners. Methodology: The investigator employed a multiple-methods study that relied on a purposive sample of graduate students enrolled in educational leadership programs at two small universities in the southeastern United States. Participants completed a two-hour module of immersive simulation designed to facilitate transfer of knowledge to skills thereby increasing their self-efficacy. Contribution: This paper contributes to a small body of literature that examines the use of immersive simulation to prepare aspiring principals. Findings: The findings indicate moderate effect sizes in changes in self-efficacy, positive attitudes toward immersive simulation as a pedagogical tool, and significance in the design of immersive simulation modules. This suggests that immersive simulation, when properly designed, aids principals in taking action to improve schools. Recommendations for Practitioners: Educational leadership programs might consider the use of immersive simulations to enhance principals’ ability to meet the complex demands of leading in the 21st century. Impact on Society: Principals may be more adept at improving schools if preparation programs provided consistent opportunities to engage in immersive simulations. Future Research: Future research should be conducted with larger sample sizes and longitudinally to determine the effectiveness of this treatment.

Aim/Purpose: The purpose of this study is to develop an efficient methodology that can assist the evaluators in assessing a variable number of individuals that are working in groups and guarantee that the assessment is dependent on the group members’ performance and contribution to the work developed. Background: Collaborative work has been gaining more popularity in academic settings. However, group assessment needs to be performed according to each individual’s performance. The problem rests on the need to distinguish each member of the group in order to provide fair and unbiased assessments. Methodology: Design Science Research methodology supported the design of a framework able to provide the evaluator with the means to distinguish individuals in a workgroup and deliver fair results. Hevner’s DSR guidelines were fulfilled in order to describe WebAVALIA. To evaluate the framework, a quantitative study was performed and the first results are presented. Contribution: This paper provides a methodological solution regarding a fair evaluation of collaborative work through a tool that allows its users to perform their own assessment and peer assessment. These are made accordingly to the user’s perspectives on the performance of each group member throughout the work development. Findings: The first analysis of the results indicates that the developed method provides fairness in the assessment of group members, delivering a distinction amongst individuals. Therefore, each group member obtains a mark that corresponds to their specific contribution to the workgroup. Recommendations for Practitioners: For those who intend to apply this workgroup assessment method, it is relevant to raise student awareness about the methodology that is going to be used. That is, all the functionalities and steps in WebAVALIA have to be thoroughly explained before beginning of the project. Then, the evaluators have to decide about the students’ intermediate voting, namely if the evaluator chooses or not to publish student results throughout the project’s development. If there is the decision to display these intermediate results, the evaluator must try to encourage collaboration among workgroup members, instead of competition. Recommendation for Researchers: This study explores the design and development of an e-assessment tool – WebAVALIA. In order to assess its feasibility, its use in other institutions or contexts is recommended. The gathering of user opinions is suggested as well. It would then be interesting to compare the findings of this study with the results from other experimentations Impact on Society: Sometimes, people develop a rejection of collaborative work because they feel exploited due to the biased evaluation results. However, the group members assessment distinction, according to each one’s performance, may give each individual a sense of fairness and reward, leading to an openness/willingness towards collaborative work. Future Research: As future work, there are plans to implement the method in other group assessment contexts – such as sports and business environments, other higher education institutions, technical training students – in other cultures and countries. From this myriad of contexts, satisfaction results would be compared. Other future plans are to further explore the mathematical formulations and the respective WebAVALIA supporting algorithms.

Aim/Purpose: The aim of the study is to test whether the perception of self-regulated learning during text reading in online teacher education is improved by using a digital tool for the use of metacognitive strategies for planning, monitoring, and self-assessment. Background: The use of self-regulated learning is important in reading skills, and for students to develop self-regulated learning, their teachers must master it. Therefore, teaching strategies for self-regulated learning in teacher education is essential. Methodology: The sample size was 252 participants with the tool used by 42% or the participants. A quasi-experimental design was used in a pre-post study. ARATEX-R, a text-based scale, was used to evaluate self-regulated learning. The 5-point Likert scale includes the evaluation of five dimensions: planning strategies, cognition management, motivation management, comprehension assessment and context management. A Generalized Linear Model was used to analyse the results. Contribution: Using the tool to self-regulate learning has led to an improvement during text reading, especially in the dimensions of motivation management, planning management and comprehension assessment, key dimensions for text comprehension and learning. Findings: Participants who use the app perceive greater improvement, especially in the dimensions of motivation management (22,3%), planning management (19.9%) and comprehension assessment (24,6%), which are fundamental dimensions for self-regulation in text reading. Recommendations for Practitioners: This tool should be included in teacher training to enable reflection during the reading of texts, because it helps to improve three key types of strategies in self-regulation: (1) planning through planning management, (2) monitoring through motivation management and comprehension assessment, and (3) self-assessment through comprehension assessment. Recommendation for Researchers: The success of the tool suggests further study for its application in other use cases: other student profiles in higher education, other teaching modalities, and other educational stages. These studies will help to identify adaptations that will extend the tool’s use in education. Impact on Society: The use of Metadig facilitates reflection during the reading of texts in order to improve comprehension and thus self-regulate the learning of content. This reflection is crucial for students’ knowledge construction. Future Research: Future research will focus on enhancing the digital tool by adding features to support the development of cognition and context management. It will also focus on how on adapting the tool to help other types of learners.

Badminton, as a fast and highly technical sport, requires high accuracy in identifying athletes' swing movements. Accurately identifying different swing movements is of great significance for technical analysis, coach guidance, and game evaluation. To improve the recognition accuracy of badminton players' swing movements, this text is based on an improved dense trajectory algorithm to improve the accuracy of recognising badminton players' swing movements. The features are efficiently extracted and encoded. The results on the KTH, UCF Sports, and Hollywood2 datasets demonstrated that the improved algorithm achieved recognition accuracy of 94.2%, 88.2%, and 58.3%, respectively. Compared to traditional methods, the innovation of research lies in optimised feature extraction methods, efficient algorithm design, and accurate action recognition. These results provide new ideas for the research and application of badminton swing motion recognition.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

An E-Portfolio Assessment Management System (EAMS) can be an innovative tool that provides students with flexible opportunities to demonstrate the acquisition of skills and abilities in an outcome-based institution. The system has been developed and used for the past ten years to create, reflect, revise, and structure students’ work. It is a repository management system that facilitates collecting, sharing, and presenting artifacts of student learning outcomes via a digital medium. Therefore, it provides students with flexible opportunities to demonstrate the acquisition of skills and abilities to demonstrate growth of achieving learning outcomes. The rationale of the EAMS is to allow students to demonstrate competences and reflect upon experiences to improve their learning and career readiness; hence, they are accountable for their learning. The system was built around two defined set of learning outcomes: institutionally agreed upon set of learning outcomes, and learning objectives that are related to major requirements. The purpose of this study is to analyze students’ perceptions and attitudes when using an e-portfolio to support their employment opportunities. The participants were 217 students in the College of Technological Innovation. The students reported that the developing of e-portfolios was extremely helpful. The results showed that students have positive opinions about using e-portfolios as a beneficial tool to support their readiness for employment; they believe an e-portfolio increases their confidence to find a job in the IT field because it can allow them to showcase artifacts that demonstrate competencies and reflect upon experiences, and they can provide their supervisors during their industrial training with an e-resume that includes views of their actual work of what they have learned and are able to do when they complete their degree. Employers then can review e-portfolios to select prospective employees work readiness skills; hence, graduates are more likely to obtain a job in their workplaces. In conclusion, students do like the idea of e-portfolios when it is presented to them as a career showcase rather than a process for documenting learning. A career center can use e-portfolios as a tool to help students find a job. Furthermore, our analysis and evaluation uncovered learning issues involved in moving from the traditional approach of learning toward an integrated learning system that can be used after graduation.

This paper will shed light on the lack of the development of appropriate monitoring systems in the field of education. Test banks can be easily purchased. Smart phones can take and share pictures of exams. A video of an exam given through Blackboard can easily be made. A survey to determine the extent of cheating using technology was given to several university students. Evidence is provided that shows security is lacking as evidenced by the number of students who have made use of technological advances to cheat on exams. The findings and conclusion may serve as evidence for administrators and policy makers to re-assess efforts being made to increase security in online testing.

Health Data collection is one of the major components of public health systems. Decision makers, policy makers, and medical service providers need accurate and timely data in order to improve the quality of health services. The rapid growth and use of mobile technologies has exerted pressure on the demand for mobile-based data collection solutions to bridge the information gaps in the health sector. We propose a prototype using open source data collection frameworks to test its feasibility in improving the vaccination data collection in Kenya. KenVACS, the proposed prototype, offers ways of collecting vaccination data through mobile phones and visualizes the collected data in a web application; the system also sends reminder short messages service (SMS) to remind parents on the date of the next vaccination. Early evaluation demonstrates the benefits of such a system in supporting and improving vaccination of children. Finally, we conducted a qualitative study to assess challenges in remote health data collection and evaluated usability and functionality of KenVACS.

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast cancer. However, it may introduce errors during detection and interpretation, leading to false-negative and false-positive results due to lack of pre-processing of it. To overcome this issue, we proposed a effective image pre-processing technique-based on Otsu's thresholding and single-seeded region growing (SSRG) to remove artefacts and segment the pectoral muscle from breast mammograms. To validate the proposed method, a publicly available MIAS dataset was utilised. The experimental finding showed that proposed technique improved 18% breast cancer detection accuracy compared to existing methods. The proposed methodology works efficiently for artefact removal and pectoral segmentation at different shapes and nonlinear patterns.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Aim/Purpose: The purpose of this paper is to introduce, describe, and document the methods involved in the preparation of a mindset intervention built into a freshmen development course, and established after years of longitudinal research, that is designed to have a positive impact on the outlook, achievement, and persistence of first generation and under-prepared students. Background: A number of studies conducted in the past fifteen years have concluded that grit, the persistence and perseverance to achieve goals, and growth mindset, the belief that skills and intelligence can be developed, are positive predictors of achievement; however, little focus has been placed on the implications at institutions purposed to educate minorities, first generation college students, and learners from diminished socio-economic backgrounds. Methodology: A series of models were created, custom self-assessment scales designed, and a lesson plan prepared purposed to deliver a mindset intervention to edify students about and change perceptions of grit, locus of control/self-efficacy, growth mindset, and goal setting. The mindset intervention, as presented in this paper, was delivered as part of a pilot implementation to students enrolled in a freshmen professional development course at a Mid-Atlantic HBCU in the Fall of 2019. Contribution: This qualitative paper documents an ongoing initiative while providing a workable template for the design and delivery of a mindset intervention that is believed will be highly effective with first generation and socio-economically disadvantaged learners. It represents the third paper in a five paper series. Findings: Prior research conducted by the authors shed light on the need to explore non-cognitive factors that may affect student performance such as grit, mindset, engagement, self-efficacy, and goal setting. The authors postulate that a carefully crafted mindset intervention delivered to freshmen students from traditionally underserved populations attending a minority serving institution in the mid-atlantic region of the United States will yield positive outcomes in terms of student success. Recommendations for Practitioners: As part of a commitment to positive student outcomes, faculty and administrators in higher education must be constantly exploring factors that may, or may not, impact student success. Recommendation for Researchers: Research is needed that explores elements that may help to contribute to the success of under prepared college students, in particular those who are from low income, first generation, and minority groups Impact on Society: Since, mindset interventions have been shown to be particularly effective with underserved students, it stands to reason that they should be adopted widely, and be effective at delivering positive outcomes, at HBCUs Future Research: The authors have introduced the mindset intervention with freshmen business students enrolled in a required professional development course. Results of the self-assessments and reflection questions are being collected and coded. Additionally, students are being administered a survey designed to measure the perceived efficacy of the initiative.

Aim/Purpose Information Technology students’ learning outcomes improve when teaching methodology moves away from didactic behaviorist-based pedagogy toward a more heuristic constructivist-based version of andragogy. Background There is a distinctive difference, a notable gap, between the academic community and the business community in their views of the level of preparedness of recent information technology program graduates. Understanding how Information Technology curriculum is developed and taught along with the underpinning learning theory is needed to address the deficient attainment of learning outcomes at the heart of this matter. Methodology The case study research methodology has been selected to conduct the inquiry into this phenomenon. This empirical inquiry facilitates exploration of a contemporary phenomenon in depth within its real-life context using a variety of data sources. The subject of analysis will be two Information Technology classes composed of a combination of second year and third year students; both classes have six students, the same six students. Contribution It is the purpose of this research to show that the use of improved approaches to learning will produce more desirable learning outcomes. Findings The results of this inquiry clearly show that the use of the traditional behaviorist based pedagogic model to achieve college and university IT program learning outcomes is not as effective as a more constructivist based andragogic model. Recommendations Instruction based purely on either of these does a disservice to the typical college and university level learner. The correct approach lies somewhere in between them; the most successful outcome attainment would be the product of incorporating the best of both. Impact on Society Instructional strategies produce learning outcomes; learning outcomes demonstrate what knowledge has been acquired. Acquired knowledge is used by students as they pursue professional careers and other ventures in life. Future Research Learning and teaching approaches are not “one-size-fits-all” propositions; different strategies are appropriate for different circumstances and situations. Additional research should seek to introduce vehicles that will move learners away from one the traditional methodology that has been used throughout much of their educational careers to an approach that is better suited to equip them with the skills necessary to meet the challenges awaiting them in the professional world.

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining

Hoffmann A, Penner J, Vohland K, Cramer W, Doubleday R, Henle K, Kõljalg U, Kühn I, Kunin WE, Negro JJ, Penev L, Rodríguez C, Saarenmaa H, Schmeller DS, Stoev P, Sutherland WJ, Tuama1 EO, Wetzel F, Häuser CL (2014) Improved access to integrated biodiversity data for science, practice, and policy - the European Biodiversity Observation Network (EU BON). Nature Conservation 6: 49–65. doi: 10.3897/natureconservation.6.6498

The "Building the European Biodiversity Observation Network" EU BON General Meeting took place between 30 March - 3 April 2014 in Heraklion on Crete, to present major project results and set objectives for the future. The meeting was preceeded by a review paper recently published in the open access journal Nature Conservation, to point out EU BON researchh interests and objectives for the future of biodiversity protection.

This is a group photo of the participants in the recent EU BON General Meeting in Crete, Greece.

The 2014 General Meeting brought together keynote speakers Jörg Freyhof (GEO BON, Executive Director), Marc Paganini (European Space Agency), Jerry Harrison (UNEP-WCMC) with the entire EU BON consortium to discuss collaborations between the project and other important initiatives in the areas of earth observation, particularly in remote sensing and in situ approaches to biodiversity data collection, as well as in the use and analysis of biodiversity data for forecasting and scenario building, and environmental policy.

"The high potential for satellite Earth Observations to support biodiversity monitoring is growing but is yet to be fully realised. The recent efforts of GEO BON, supported by the GEO Plenary and the CBD Conference of the Parties, to define a set of minimum essential observational requirements to monitor biodiversity trends will give considerable impetus for space agencies and for the remote sensing community to focus their work on a small set of well defined earth observations products that will serve the needs of the biodiversity community at large. In that context ESA is firmly engaged in supporting the development of these emerging Essential Biodiversity Variables (EBVs). EU BON together with ESA can be pioneers in the early development and demonstration." comments Marc Paganini, European Space Agency, on the future collaboration between the two initiatives.

The world's biodiversity is in an ongoing dramatic decline that despite conservation efforts remains unprecedented in its speed and predicted effects on global ecosystem functioning and services. The lack of available integrated biodiversity information for decisions in sectors other than nature conservation has been recognized as a main obstacle and the need to provide readily accessible data to support political decisions has been integrated into the CBD's "Strategic Plan for Biodiversity 2011–2020" and the Aichi targets. The recently published EU BON review paper points out how the project will use its potential to improve the interaction between citizens, science and policy for a better future of biodiversity protection.

EU BON aims to enable decision makers at various levels to make use of integrated and relevant biodiversity information adapted to their specific requirements and scales. Disparate and unconnected databases and online information sources will be integrated to allow improved monitoring and evaluation of biodiversity and measures planned or taken at different spatial and temporal scales. This requires strong efforts not only with regard to technical harmonization between databases, models, and visualization tools, but also to improve the dialogue between scientific, political, and social networks, spanning across several scientific disciplines as well as a variety of civil science organizations and stakeholder groups.

The project is focusing mainly on the European continent but contributes at the same time to the globally oriented Group on Earth Observations Biodiversity Observation Network (GEO BON), which itself contributes to the Group of Earth Observation System of Systems (GEOSS). EU BON will build on existing information infrastructures such as GBIF, LifeWatch and national biodiversity data centres in Europe, and will integrate relevant biodiversity data from on-ground observations to remote sensing information, covering terrestrial, freshwater and marine habitats.

Original Source:

Hoffmann A, Penner J, Vohland K, Cramer W, Doubleday R, Henle K, Kõljalg U, Kühn I, Kunin WE, Negro JJ, Penev L, Rodríguez C, Saarenmaa H, Schmeller DS, Stoev P, Sutherland WJ, Ó Tuama É, Wetzel FT, Häuser CL (2014) Improved access to integrated biodiversity data for science, practice, and policy - the European Biodiversity Observation Network (EU BON). Nature Conservation 6: 49–65. doi: 10.3897/natureconservation.6.6498

Being voluntary, citizen science work is often automatically assumed to also be openly available. Contrary to the expectations, however, a recent study of the datasets available from volunteers on the Global Biodiversity Information Facility (GBIF) prove to be among the most restrictive in how they can be used.

There is a high demand for biodiversity observation data to inform conservation and environmental policy, and citizen scientists generate the vast majority of terrestrial biodiversity observations. The analysis on GBIF showed that citizen science datasets comprise 10% of datasets on GBIF, but actually account for the impressive 60% of all observations.

Invaluable as a resource for conservationists and biodiversity scientists, however, these resources unfortunately often come with restrictions for re-use. Although the vast majority of citizen science datasets did not include a license statement, as a whole, they ranked low on the openness of their data.

The assumption that voluntary data collection leads to data sharing is not only not reflecting the real situation, but also does not recognize the wishes and motivations of those who collect data, nor does it respects the crucial contributions of these data to long-term monitoring of biodiversity trends.

In a recent commentary paper, published in the Journal of Applied Ecology, EU BON partners suggest ways to improve data openness. According to the researchers citizen scientists should be recognised in ways that correspond with their motivations, in addition its is advisable that organisations that manage these data should make their data sharing policies open and explicit.

Original Research:

Groom, Q., Weatherdon, L. & Geijzendorffer, I. (2016) Is citizen science an open science in the case of biodiversity observations? Journal of Applied Ecology. DOI: 10.1111/1365-2664.12767

A new EU BON derived paper looks at the dynamics of science - policy dialogue, offering tips for improving the uptake of scientific knowledge.

Scientific knowledge is considered to be an important factor (alongside others) in environmental policy-making. However, the opportunity for environmentalists to influence policy can often occur within short, discrete time windows. Therefore, a piece of research may have a negligible or transformative policy influence depending on when it is presented.

These ‘policy windows’ are sometimes predictable, such as those dealing with conventions or legislation with a defined renewal period, but are often hard to anticipate. We describe four ways that environmentalists can respond to policy windows and increase the likelihood of knowledge uptake: 1) foresee (and create) emergent windows, 2) respond quickly to opening windows, 3) frame research in line with appropriate windows, and 4) persevere in closed windows. These categories are closely linked; efforts to enhance the incorporation of scientific knowledge into policy need to harness mechanisms within each.

In their new reseach the authors illustrate the main points with reference to nature conservation, but the principles are widely applicable. The open access paper is available here: http://www.sciencedirect.com/science/article/pii/S1462901117302095

Read also the article published on it by the British Ecological Society: http://www.britishecologicalsociety.org/windows-opportunity-influence-policy-four-tips-improve-uptake-scientific-knowledge/

EKLIPSE is an EU-funded project that aims to develop a mechanism for supporting better informed decisions about our environment based on the best available knowledge. This short video (4 minute) explains the EKLIPSE process and you can find out more about our science-policy activities on the EKLIPSE website. The project now invites you to describe your views on how to improve the science-policy interface related to biodiversity and ecosystem services and potential ways in which you, or your background organization, would like to contribute to the EKLIPSE mechanism.

Have your say here!