Data Analysis Phase Research Articles

Proposal of geographic information systems methodology for quality control procedures of data obtained in naturalistic driving studies

The primary goal of naturalistic driving studies is to provide a comprehensive observation of the driver's behaviour under real-life conditions by measureing a great number of parameters at high temporal frequencies. Achieving this goal, however, is a complex endeavor that faces many challenges such as the complexity of the vehicle instrumentation during the phase of data collection, and the difficult handling of large data volumes during the phase of data analysis. These drawbacks often cause episodes of data losses. Improving the technical aspects of the collection of naturalistic data is of paramount importance to increase the return of the investment made in it. An aspect to consider is the control of the quality of data obtained. The procedures commonly applied often present a high workload and low levels of reliability in the results. For this reason, new and more efficient procedures should be implemented. This study proposes an innovative methodology for the quality control of these data through geographic information systems (GIS), which permit a fast, efficient and effective data evaluation. Relative and absolute errors can be located through cartographic representation obtained with GIS. Some graphical examples about the detection of the errors are shown.

Deep Mutational Scanning: Calculating Enrichment Scores for Protein Variants from DNA Sequencing Output Files.

During a deep mutational scanning experiment, a collection of variants of a given protein is subjected to high-throughput sequencing before and after selection. The variants that perform well during selection will increase in abundance, whereas those that perform poorly will decrease. Generating a sequence-function map of a protein from a deep mutational scan requires the calculation and comparison of the enrichment scores for each protein variant, based on the results of high-throughput DNA sequencing output files. Here we describe the use of the software program Enrich, which was written specifically for the data analysis phase of a deep mutational scanning experiment.

The TimeStudio Project: An open source scientific workflow system for the behavioral and brain sciences.

This article describes a new open source scientific workflow system, the TimeStudio Project, dedicated to the behavioral and brain sciences. The program is written in MATLAB and features a graphical user interface for the dynamic pipelining of computer algorithms developed as TimeStudio plugins. TimeStudio includes both a set of general plugins (for reading data files, modifying data structures, visualizing data structures, etc.) and a set of plugins specifically developed for the analysis of event-related eyetracking data as a proof of concept. It is possible to create custom plugins to integrate new or existing MATLAB code anywhere in a workflow, making TimeStudio a flexible workbench for organizing and performing a wide range of analyses. The system also features an integrated sharing and archiving tool for TimeStudio workflows, which can be used to share workflows both during the data analysis phase and after scientific publication. TimeStudio thus facilitates the reproduction and replication of scientific studies, increases the transparency of analyses, and reduces individual researchers’ analysis workload. The project website (http://timestudioproject.com) contains the latest releases of TimeStudio, together with documentation and user forums.

Seniors managing multiple medications: using mixed methods to view the home care safety lens.

BackgroundPatient safety is a national and international priority with medication safety earmarked as both a prevalent and high-risk area of concern. To date, medication safety research has focused overwhelmingly on institutional based care provided by paid healthcare professionals, which often has little applicability to the home care setting. This critical gap in our current understanding of medication safety in the home care sector is particularly evident with the elderly who often manage more than one chronic illness and a complex palette of medications, along with other care needs. This study addresses the medication management issues faced by seniors with chronic illnesses, their family, caregivers, and paid providers within Canadian publicly funded home care programs in Alberta (AB), Ontario (ON), Quebec (QC) and Nova Scotia (NS).MethodsInformed by a socio-ecological perspective, this study utilized Interpretive Description (ID) methodology and participatory photographic methods to capture and analyze a range of visual and textual data. Three successive phases of data collection and analysis were conducted in a concurrent, iterative fashion in eight urban and/or rural households in each province. A total of 94 participants (i.e., seniors receiving home care services, their family/caregivers, and paid providers) were interviewed individually. In addition, 69 providers took part in focus groups. Analysis was iterative and concurrent with data collection in that each interview was compared with subsequent interviews for converging as well as diverging patterns.ResultsSix patterns were identified that provide a rich portrayal of the complexity of medication management safety in home care: vulnerabilities that impact the safe management and storage of medication, sustaining adequate supports, degrees of shared accountability for care, systems of variable effectiveness, poly-literacy required to navigate the system, and systemic challenges to maintaining medication safety in the home.ConclusionsThere is a need for policy makers, health system leaders, care providers, researchers, and educators to work with home care clients and caregivers on three key messages for improvement: adapt care delivery models to the home care landscape; develop a palette of user-centered tools to support medication safety in the home; and strengthen health systems integration.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-015-1193-5) contains supplementary material, which is available to authorized users.

Encourage Playing with Data and Discourage Questionable Reporting Practices.

Not so long ago, in the early days of experimental psychology, researchers performed their data analyses by hand (or paid people to do these calculations for them). No wonder, they took ample time beforehand to think through which analyses to perform (and especially which not). Exploring data was not really an option. The t value or F value that resulted from the calculations provided a more or less definitive answer. H0 could be rejected or not. During the past decades, we have quickly moved from punch cards, via expensive mainframes, to relatively cheap but powerful personal computers that are fulltime available to individual researchers and students. Nowadays, SPSS and other user-friendly statistical packages make it easy to run all types of analyses, which encourage analyzing your data in every possible way. It is stunningly easy to (re-)run analyses with or without a specific group of participants, with or without an additional between-subject condition, with or without an extra variable in a scale. Indeed, it may be a waste of resources to leave a dataset partly unexplored when SPSS offers all the tools to do so. In many research labs, analyzing results has shifted from spending days to do confirmatory analyses by hand, to spending days engaging in all kinds of data analyses to see what’s in the data.” Is this a bad thing? We do not think so, necessarily. Given that you know what you are doing in SPSS, you can learn a lot from exploring a dataset. However, there clearly are risks (see Simmons, Nelson & Simonsohn, 2011), which we will revisit later. This contribution is a commentary on Playing with Data—Or How to Discourage Questionable Research Practices and Stimulate Researchers to Do Things Right” by Sijtsma (2015). In his paper, Klaas Sijtsma makes the important point that prevention of research practices may be more important than detection. He suggests two policy measures. First, make research data and research materials publicly available. Second, encourage researchers to consult methodologists or statisticians for help and a second opinion. In this commentary, we will not reiterate the points made by Klaas Sijtsma. We support his analysis and his policy recommendations. Making research data and research materials publicly available and consulting methodologists and statisticians will help in increasing the transparency of the data analysis phase of the empirical research cycle within psychology. Currently, in too many cases, this phase is the exclusive domain of (small groups of) individual researchers and their personal computers. However, making the data analysis phase transparent to others will not prevent scientific fraud. A person who willingly aims to fool others always will be able to do so, also when sharing (well-faked) data. Nevertheless, it will help in reducing the errors researchers may make unwittingly due to incorrect methodological decisions and the incorrect use of statistical methods (see Sijtsma). The current contribution focuses on what we think is quickly becoming a misnomer in our field, namely the use of the phrase questionable research practices” for certain types of practices when analyzing and reporting data. We suggest to replace this with the, in our view, more accurate phrase questionable reporting practices,” in relation to most potentially problematic data analysis strategies. The abbreviation may remain the same, namely QRPs. However, the meaning and use of the phrase will be more positive and more encouraging to junior and senior researchers, and will do more justice to the important distinction between confirmation and exploration when analyzing data and reporting results (see Wagenmakers, 2012).

Variable ranking based on the estimated degree of separation for two distributions of data by the length of the receiver operating characteristic curve

Variable responses are fundamental for all experiments, and they can consist of information-rich, redundant, and low signal intensities. A dataset can consist of a collection of variable responses over multiple classes or groups. Usually some of the variables are removed in a dataset that contain very little information. Sometimes all the variables are used in the data analysis phase. It is common practice to discriminate between two distributions of data; however, there is no formal algorithm to arrive at a degree of separation (DS) between two distributions of data. The DS is defined herein as the average of the sum of the areas from the probability density functions (PDFs) of A and B that contain a≥percentage of A and/or B. Thus, DS90 is the average of the sum of the PDF areas of A and B that contain ≥90% of A and/or B. To arrive at a DS value, two synthesized PDFs or very large experimental datasets are required. Experimentally it is common practice to generate relatively small datasets. Therefore, the challenge was to find a statistical parameter that can be used on small datasets to estimate and highly correlate with the DS90 parameter. Established statistical methods include the overlap area of the two data distribution profiles, Welch’s t-test, Kolmogorov–Smirnov (K–S) test, Mann–Whitney–Wilcoxon test, and the area under the receiver operating characteristics (ROC) curve (AUC). The area between the ROC curve and diagonal (ACD) and the length of the ROC curve (LROC) are introduced. The established, ACD, and LROC methods were correlated to the DS90 when applied on many pairs of synthesized PDFs. The LROC method provided the best linear correlation with, and estimation of, the DS90. The estimated DS90 from the LROC (DS90–LROC) is applied to a database, as an example, of three Italian wines consisting of thirteen variable responses for variable ranking consideration. An important highlight of the DS90–LROC method is utilizing the LROC curve methodology to test all variables one-at-a-time with all pairs of classes in a dataset.

Cultural challenges to implementation of formative assessment in Saudi Arabia: An exploratory study

Background/Purpose: This study investigates challenges that students and faculty face to implement assessment for learning; and the activities, capabilities, enablers, and indicators which could impact performance.Method: The study is a mixed methods research, cross-sectional, exploratory study. The study was organized through two phases of data collection and analysis (QUAL → quan). Based on qualitative focus group discussions (FGD), we first gathered data through field notes. Later, we engaged in analysis using techniques drawn from qualitative data including categorization, theme identification, and connection to existing literature. Based on this analysis, we developed a questionnaire that could provide quantitative measures based on the qualitative FGD. We then administered the questionnaire, and the quantitative data were analyzed to quantitatively test the qualitative findings. Twenty-four faculty and 142 students from the 4th and 5th clinical years participated voluntarily. Their perception of FA and the cultural challenges that hinder its adoption were evaluated through a FGD and a questionnaire.Results: The mean score of understanding FA concept was equal in faculty and students (p = 0.08). The general challenge that scored highest was the need to balance work and academic load in faculty and the need to balance study load and training and mental anxiety in students. There was no difference between faculty and students in perceiving “learning is teacher-centered” (p = 0.481); and “past learning and assessment experience” (p = 0.322). There was a significant difference between them regarding interaction with opposite gender (p <0.001). Students showed higher value as regards the “gap between learning theories and assessment practice”, “grade as a priority”, and “discrimination by same faculty gender”.Conclusion: The authors suggested a “Framework of Innovation in Endorsing Assessment for Learning”. It emphasizes a holisitic approach through all levels of the System: Government, Accreditation Bodies, Policy makers; Institution, and Classroom levels.

Rape Survivors' Experiences of the Silent Protest: Implications for Promoting Healing and Resilience.

This article examines the experiences of nine rape survivors who participated in the Silent Protest, an annual protest march at Rhodes University that aims to highlight the sexual abuse of women, validate the harm done, and foster solidarity among survivors. Participants responded to a semi-structured interview focusing on the context of their rape and its impact, and their experiences of participation in the Protest In the first phase of data analysis, synoptic case narratives were written. In the second, themes from participants' experience were identified using interpretative phenomenological analysis. In the third, the data were examined in light of questions around the extent to which participation contributed to healing. Participants reported experiences of validation and empowerment but the majority were suffering from posttraumatic stress disorder. In some cases, participation had exacerbated self-blame and avoidant coping. Recommendations are made about the provision of psychoeducation and counseling at such events.

Supporting staff to respond effectively to informal complaints: findings from an action research study.

To understand how nurses and midwives manage informal complaints at ward level. The provision of high quality, compassionate clinical nursing and midwifery is a global priority. Complaints management systems have been established within the National Health Service in the UK to improve patient experience yet little is known about effective responses to informal complaints in clinical practice by nurses and midwives. Collaborative action research. Four phases of data collection and analysis relating to primarily one National Health Service trust during 2011-2014 including: scoping of complaints data, interviews with five service users and six key stakeholders and eight reflective discussion groups with six midwives over a period of nine months, two sessions of communications training with separate groups of midwives and one focus group with four nurses in the collaborating trust. Three key themes emerged from these data: multiple and domino complaints; ward staff need support; and unclear complaints systems. Current research does not capture the complexities of complaints and the nursing and midwifery response to informal complaints. Robust systems are required to support clinical staff to improve their response to informal complaints and thereby improve the patient experience.

Leveraging The Data Gathering and Analysis Phases to Gain Situational Awareness

Situational Awareness (SA) is the process of collecting observations, understanding their meaning and projection of new possible updates. It is applied in several dynamic and complex environments like aviation, air traffic control, power plant operations, military command and control. Object classification in infrared images is a crucial component of smart surveillance systems. In this paper, a robust framework for person-vehicle classification is proposed, which classifies objects with 97.2% accuracy and successfully achieves its intended use in challenging real-world situations. Some of these challenges are: Random camera viewpoints and low resolution infrared images. The main concern of this framework is enhancing the preprocessing phase to achieve higher classification accuracy. This paper provides an improved classification process accompanied with interpolation process for extracting the concerned features (objects) in infrared images. This framework increases the classification accuracy and overcom...

Factor Analysis of Agricultural Development Indicators from Iranian Agriculture Experts' Viewpoints

Agriculture and its development is the foundation of development in Iran as a developing country. So, it can be regarded as the foundation for economical and social development. The capabilities of agriculture sector are limited and its efficiency is trivial because of neglecting agricultural development and keeping its support just as a slogan. The trans- formation of agriculture to a developed, dynamic, efficient environment depends not only on appropriate climate and natural resources but also on human resource development in the relevant sector. The main purpose of the present research was to study and recognize agricultural development indicators from agriculture experts’ viewpoints (including researchers, trainers and extension experts) in six provinces in Iran on the basis of Adjacent Provinces Plan. The study was designed with three phases of theoretical foundations, field operations and data analysis. The statistical population was 863 experts, out of which 198 experts were selected by stratified sampling. The validity and reliability of measurement tool (questionnaire) was analyzed by SPSS software package. The study was a correlation-descriptive study in which factor analysis statistics was used in addition to descriptive statistics. Experts grouped indicators of future agricultural development in nine groups (access to inputs, application of technologies for the development of human resource and sustainability; reduction of losses; economical development; improvement of infrastructures; agricultural mechanization; social status; improvement of marketing; land reform; yield increase). Results about the difference in respondents’ viewpoints revealed significant differences in experts’ viewpoints in six studied province about relevant variables and in their viewpoints about the components of agricultural development (infrastructure improvement, marketing, optimum management and sustainability, human resource development and economical development).

Qualitative Research Methods in Sport, Exercise and Health: From process to product

Qualitative forms of inquiry are a dynamic and exciting area within contemporary research in sport, exercise and health. Students and researchers at all levels are now expected to understand qualitative approaches and be able to employ them in their work. In this comprehensive and in-depth introductory text, Andrew C. Sparkes and Brett Smith take the reader on a journey through the entire qualitative research process that begins with the conceptualization of ideas and the planning of a study, moves through the phases of data collection and analysis, and then explains how findings might be represented in various ways to different audiences. Ethical issues are also explored in detail, as well as the ways that the goodness of qualitative research might be judged by its consumers. The book is based on the view that researchers need to make principled, informed and strategic decisions about what, why, when, and how to use qualitative forms of inquiry. The nature of qualitative research is explained in terms of both its core assumptions and what practitioners actually do in the field when they collect data and subject it to analysis. Each chapter is vividly illustrated with cases and examples from published research, to demonstrate different qualitative approaches in action and their relative strengths and weaknesses. The book also extends the boundaries of qualitative research by exploring innovative contemporary methodologies and novel ways to report research findings. Qualitative Research Methods in Sport, Exercise and Health is essential reading for any student, researcher or professional who wishes to understand this form of inquiry and to engage in a research project within a sport, exercise or health context.

LKM: A LDA-BasedK-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks

Mobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimension space, great pressure has been put on the subsequent data analysis and response phase. Therefore, traditional methods for intrusion detection can no longer be applicable in MSNs. To improve the performance of data analysis, we applyK-means algorithm to high-dimension data clustering analysis. Thus, an improvedK-means clustering algorithm based on linear discriminant analysis (LDA) is proposed, called LKM algorithm. In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we useK-means algorithm for clustering analysis of the dimension-reduced data. Simulation results show that LKM algorithm shortens the sample feature extraction time and improves the accuracy ofK-means clustering algorithm, both of which prove that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of IDS in MSNs.

경구피임약을 복용하는 한국여성과 치주질환의 관련성 연구: 제5기 3차년도(2012) 국민건강영양조사 자료 분석

Objectives: The purpose of the study was to investigate the relationship between the oral contraceptive pill and periodontal disease in Korean women aged from 19 to 50 years old. Methods: This study selected 1,579 women of childbearing age from 19 to 50 years old from the 5th National Health and Nutrition Survey data analysis(phase 3, 2012) taking the oral examination. The questionnaire consisted of socioeconomic demographic characteristics, health behavior, use of oral contraceptive pills, and periodontal disease. Socioeconomic demographic characteristics included age, marital status, education, monthly income, and vocation. The health behavior included obesity, stress, smoking, subjective oral health status, use of dental floss, tooth brushing, and diabetes mellitus by fasting blood sugar level. Use of oral contraceptive pills was recorded by monthly use and duration. The periodontal disease was documented by yes or no and selected as dependent variable by logistic regression analysis. Results: After revising the taking period of oral contraceptive pill for this study, there was the correlation between the prevalence of periodontal disease and odds ratio(95% CI) 1.288(1.027-1.617). Conclusions: This study will contribute to the direction of policy for an oral contraceptive pill and provide the basic data for counseling for the oral health and the side effects of oral contraceptive pills.

Statistics: A Life Cycle View

Statistics has gained a reputation as being focused only on data collection and data analysis. This paper is about an expanded view of the role of statistics in research, business, industry and service organizations. Such an approach provides an antidote to the narrow view of statistics outlined above. The life cycle view we elaborate on can contribute to close the gap between theory and practice and improve the position of statistics as a scientific discipline with wide relevance to organizations and research activities. Specifically we discus here a “life cycle view” consisting of: 1) Problem elicitation, 2) Goal formulation, 3) Data collection, 4) Data analysis, 5) Formulation of findings, 6) Operationalization of findings, 7) Communication and 8) Impact assessment. These 8 phases are conducted with internal iterations that combine the inductive-deductive learning process studied by George Box (Box, 1997). Covering these 8 dimensions, beyond the data analysis phase, increases the impact of statistical analysis and enhances the level of generated knowledge and information quality it leads to. The envisaged overall approach is that applied statistics needs to involve a trilogy combining: 1) a life cycle view, 2) an analysis of impact and 3) an assessment of the quality of the generated information and knowledge. We begin with a section introducing the problem, continue with a review of the InfoQ concept presented in Kenett and Shmueli (2013) and proceed with a description of the eight life cycle phases listed above. Adopting a life cycle view of statistics has obvious implications to research, education and statistical practice. We conclude with a discussion of such implications.

Statistics: A Life Cycle View

ABSTRACTStatistics has gained a reputation as being focused only on mathematical modeling, data collection, and data analysis. This article is about an expanded view of the role of statistics in research, business, industry, and service organizations. Such an approach provides an antidote to the narrow view of statistics outlined above in an effort to reposition it as the leading profession in the analytics domain. This view creates a need for focused research activities and the development of new methods and tools, often in collaboration of statisticians with experts in other disciplines (Kenett 2012). Specifically we discus here a “life cycle view” consisting of (1) problem elicitation, (2) goal formulation, (3) data collection, (4) data analysis, (5) formulation of findings, (6) operationalization of findings, (7) communication, and (8) impact assessment. These eight phases, with internal iterations, combine the inductive–deductive learning process studied by George Box (1997). Covering these phases, beyond the data analysis phase, increases the impact of statistical analysis and enhances the level of generated knowledge and information quality. The envisaged overall approach presented here is that the research and practice of applied statistics needs to involve a trilogy combining (1) a life cycle view, (2) an analysis of impact, and (3) an assessment of the quality of the generated information and knowledge. We begin with a section introducing the problem, continue with a review of the information quality (InfoQ) concept presented in Kenett and Shmueli (2014), and proceed with a description of the eight life cycle phases listed above. Adopting a life cycle view of statistics has obvious implications to research, education, and statistical practice. These are presented in the context of several examples. We conclude with a discussion of such implications.

Modern approaches to processing large hyperspectral and multispectral aerospace data flows

We consider approaches to processing large hyperspectral and multispectral imaging flows produced in aerospace monitoring for solving a wide range of problems of management of natural resources, environmental security, prevention of natural disasters and technogenic accidents, as well as problems of real economy, and basic and applied sciences. We analyze the specific features of the phases of hyperspectral data analysis and describe a software and hardware system that uses new and improved methods and algorithms for processing large flows of hyperspectral and other aerospace data and has a high-performance computer. This system contains different types of software for identifying the types of given objects by solving inverse problems of remote sensing as well as by analyzing their qualitative and quantitative characteristics, combined multiparameter processing of hyperspectral aerospace data, tracking the local changes including those related to changes in meteorological conditions and vegetation periods, detecting and identifying the types of small objects on the basis of analysis of individual parts of the image, detecting and identifying heat sources, etc. We bring examples of processing of hyperspectral and multispectral satellite images with the help of software and hardware tools developed.

Fairness in the Learning Environment and Satisfaction with Life: A Study of Turkish University Students of Sports Sciences

The present study aims to examine university-level sports sciences students' fairness perceptions with regard to their learning environment, as well as theirsatisfaction with life in terms of certain variables, and to identify the relationship between these variables. The participants were 1042 students (430 female, 612 male) studying in the Schools of Physical Education and Sports at 9 different universities. Turkish versions of the Fair Learning Environment Questionnaire and Satisfaction with Life Scale were used as data gathering tools. In the data analysis phase, a t-test, one-way ANOVA and Pearson correlation were employed. The results revealed that the students perceived the learning environments as partially fair, and their satisfaction with life was at moderate level. It was also noticed that female students' fairness perceptions towards the learning environment were significantly lower than male students; however, there was no significant difference in their satisfaction with life. A significant difference was found in fairness perceptions and satisfaction with life based on the year of study. Overa ll, there was a significant positive correlation between the students' fairness perceptions toward their learning environment and their satisfaction with life.

Abstract A56: Adaptation of a cancer clinical trials educational program for African Americans and Latinos

Abstract The purpose of this study was to educate Latino and African American urban populations about cancer clinical trials. Compared to whites, both African Americans and Latinos tend to have lower access to cancer treatment and to be diagnosed at advanced stages, trends associated with increased mortality and more difficult treatments. Clinical trials offer the possibility of developing improved treatments and participants may access treatments before they are widely available. Yet, while overall 3-5% of cancer patients participate in clinical trials, a lower proportion of ethnic minorities participate, resulting in limited generalizability of outcome data on the effectiveness of interventions to prevent and control cancer among underrepresented groups, and less access to newer treatments. Previous research has identified barriers to minority participation in clinical research related to scientific literacy (including fear of mistreatment), language, and civic literacy (lack of trust in biomedical systems). This paper describes the adaptation of a community-based cancer clinical trials educational program from the West Coast of the United States to the East Coast. Researchers and program staff from the two sites worked closely together to share materials, including the pre- and post-program questionnaires and the curriculum formatted in PowerPoint. The program curriculum was revised to be consistent with contextual factors, in order to be culturally competent in the adapting region, as follows. Minority cancer clinical trial participants who were representative of local populations were invited to participate in videotaped interviews about their research experiences. Taped segments of local interview subjects were integrated into the presentation, and segments featuring West Coast research participants were removed. The Spanish language in the curriculum was revised to match regional usage of Latino groups dominant in the East Coast region, while attempting to maintain fidelity with the original meaning. Community outreach, program delivery, and data collection methods were revised to be compatible with the capacity of the adapting site, including revising the measures to match Spanish-language changes made in the curriculum and shifting from written questionnaires to Audience Response electronic technology. Every possible effort was made to otherwise maintain fidelity during implementation and measurement. In the data analysis phase, the research staff is sharing syntax for variable recoding and creation, and will discuss and compare findings across both sites. To test the feasibility of the adaptation, a recruitment goal of 120 participants (40 African Americans, 40 English-speaking Latinos, and 40 Spanish speaking Latinos) was planned. To meet our recruitment goals, over all, 143 people attended our replication programs. To measure participant responsiveness, participants were asked to provide open-ended written comments. The vast majority expressed satisfaction with the presentation. The main themes identified from their comments were that the program was interesting, important, and useful, and that they learned something new. Negative feedback regarding the presentation was minor and related to technical difficulties which prolonged the meetings. We discuss the procedures and lessons learned during the adaptation and feasibility testing of this intervention. This study demonstrates that adaptation of a cancer clinical trials education program from one geographic region to another is feasible, and that the process and the initiative benefit from the sharing of materials between multiple sites. These data contribute to the growing literature on dissemination and implementation research in cancer control and prevention. Citation Format: Debra J. Pelto, Georgia Robins Sadler, Ogo Njoku, Maria Carina Rodriguez, Alma Behar, Vanessa L. Malcarne, Lina Jandorf. Adaptation of a cancer clinical trials educational program for African Americans and Latinos. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr A56. doi:10.1158/1538-7755.DISP13-A56

Increasing Stocking Density Reduces Emissions of Fugitive Dust from Cattle Feedyards

<abstract><title><italic>Abstract. </italic></title> The moisture and compaction dynamics of an open-lot corral surface in a cattle feedyard depend strongly on the spatial density at which the animals are stocked. Because the moisture content and bulk density of the corral surface directly influence its intrinsic dust susceptibility, emission of fugitive dust from a feedyard surface should be sensitive to changes in stocking density. In the summer of 2012 we measured airborne dust concentrations upwind and downwind of feedyard pens stocked at two different densities, 718 (control) and 1,435 hd ha^-1, over a 160-d feeding period. Doubled stocking density was achieved in two different ways, by (A) confining cattle to half the pen area using electric cross-fencing and (B) doubling the number of cattle in the pens. Path-averaged dust concentrations were measured upwind and downwind of feedyard pens using an optical particle sizer (OPS model 3330, TSI Inc., Shoreview, Minn.) and an earlier-model optical sensor (model DUSTTRAK II 8530, TSI Inc., Shoreview, Minn.) mounted on mobile monitoring platforms. Because the monitoring platforms used different instruments, during the data analysis phase DUSTTRAK data were compared only to DUSTTRAK data, and OPS data were compared only to OPS data. Downwind 1-min concentrations of dust varied from 1 to 4,478 μg m^-3 for the control pens, 1 to 2,431 μg m^-3 for the pens with cross-fencing (treatment A), and 1 to 2,872 μg m^-3 for the pens with twice as many cattle as the control pens (treatment B). Dispersion modeling using AERMOD revealed that the apparent dust-emission fluxes from treatments A and B were 79.4% and 80.6% lower, respectively, than the apparent emission flux from the control pens (23.45 μg m^-2 s^-1). We conclude that stocking-density manipulation is likely to be a viable Beneficial Management Practice (BMP) for controlling fugitive dust from open-lot cattle feedyards but that improved, path-averaged monitoring techniques appropriate to the feedyard source geometry are needed.