trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language

An Investigation of How Surface Coal Mining Affects Water Quality.

Surface coal mining has become the ideal method for extracting coal from the Appalachia Mountains. However, surface coal mining generates large amounts of waste which may decrease the water quality in central Appalachia. This research is an attempt to determine whether surface coal mining negatively impacts water quality. This research consists of a literature review in addition to an analysis of data obtained through the Virginia Department of Environmental Quality. This data was analyzed at three separate locations along the Clinch River, VA to determine trends and cycles in pH, temperature, total hardness, and chloride, sulfate and metal concentrations. After analysis of data, it was concluded surface mining did not negatively impact water quality at these three locations. In addition, more research must be done to make a more accurate, concise conclusion between water quality and surface mining

Pending Charges

pages 99-10

Halcyon Days

pages 97-9

A Mixed Methods Approach To Understanding The Juvenile Re-Entry Mentoring Process

Juvenile mentoring programs are an institution of informal social control that through programmatic design intends to mitigate delinquent behaviors with the development of strong quality social bonds. In these programs, mentees involved in the juvenile justice system are matched with older mentors to form social bonds as a method of encouraging prosocial behaviors. The Juvenile Reentry Mentoring Project (JRMP) is one such mentoring program matching juvenile mentees in the justice system with undergraduate mentors. Research is clear that the longer the match relationship, the stronger the relationship (Rhodes, 2007; Garringer et al., 2017). Yet, research is limited as to the program and relationship factors contributing to lasting quality relationships specific to juvenile reentry mentees (Bazron et al., 2017; Tolan et al., 2014; DuBois et al., 2006; Abrams et al., 2014). Elements understudied include the mentor’s approach to the match, mentor and mentee characteristics, and the dosage needed to produce a longlasting quality relationship. This study attempted to better understand whether these factors contributed to the quality and length of a match relationship for matches in the JRMP. I used an exploratory sequential mixed methods research design to evaluate the potential contributing factors. Given the limitations of the study, particularly relevant the sample size for analysis, findings identified various factors potentially contributing to the quality and length of a match relationship. The results provide insight and direction for improved data collection and future research

Finding the signal in the noise: Could social media be utilized for early hospital notification of multiple casualty events?

IntroductionDelayed notification and lack of early information hinder timely hospital based activations in large scale multiple casualty events. We hypothesized that Twitter real-time data would produce a unique and reproducible signal within minutes of multiple casualty events and we investigated the timing of the signal compared with other hospital disaster notification mechanisms.MethodsUsing disaster specific search terms, all relevant tweets from the event to 7 days post-event were analyzed for 5 recent US based multiple casualty events (Boston Bombing [BB], SF Plane Crash [SF], Napa Earthquake [NE], Sandy Hook [SH], and Marysville Shooting [MV]). Quantitative and qualitative analysis of tweet utilization were compared across events.ResultsOver 3.8 million tweets were analyzed (SH 1.8 m, BB 1.1m, SF 430k, MV 250k, NE 205k). Peak tweets per min ranged from 209-3326. The mean followers per tweeter ranged from 3382-9992 across events. Retweets were tweeted a mean of 82-564 times per event. Tweets occurred very rapidly for all events (<2 mins) and represented 1% of the total event specific tweets in a median of 13 minutes of the first 911 calls. A 200 tweets/min threshold was reached fastest with NE (2 min), BB (7 min), and SF (18 mins). If this threshold was utilized as a signaling mechanism to place local hospitals on standby for possible large scale events, in all case studies, this signal would have preceded patient arrival. Importantly, this threshold for signaling would also have preceded traditional disaster notification mechanisms in SF, NE, and simultaneous with BB and MV.ConclusionsSocial media data has demonstrated that this mechanism is a powerful, predictable, and potentially important resource for optimizing disaster response. Further investigated is warranted to assess the utility of prospective signally thresholds for hospital based activation

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

My favorite subject is lengua because the teacher es un crack : translanguaging in CLIL student writing

We interpret CLIL as bilingual education inasmuch as it is can help create bilinguals; and we are interested in the behaviour of emergent bilinguals. We also subscribe to the idea of holistic linguistic repertoires instead of separable languages. In this research we partially replicate research conducted by Celaya (2008) and Agustín-Llach (2009) in order to explore instances of translanguaging in CLIL writing. We focus on three categories of L1-infused language: borrowing, translating and foreignizing. Although they have previously been treated as errors, we suggest teachers could more usefully consider them as naturally occurring communicative strategies: snapshots of emergent bilingualism in their students. We compare two datasets of student writing gathered at a 3.5-year interval and discuss the evolution of their competence as evidenced in the texts they produce.Interpretem AICLE com a educació bilingüe atès que contribueix a crear bilingües; i ens interessa el comportament dels bilingües emergents. Ens adherim també a la idea de repertoris lingüístics holístics en comptes de llengües separables. En aquest estudi seguim en part la investigació realitzada per Celaya (2008) i Agustín-Llach (2009), per tal d'explorar exemples de translanguaging (designat en alguns casos com a "transllenguar") en la producció escrita d'alumnat AICLE. Ens centrem en tres categories de llenguatge infós per la L1: préstec, traducció i estrangerització. Si bé s'han considerat anteriorment com a errades, suggerim que seria més útil que el professorat les tractés com a estratègies comunicatives que sorgeixen de manera natural, és a dir, com a manifestació del bilingüisme emergent de l'alumnat. Comparem dues bases de dades de producció escrita de l'alumnat recollides en un interval de 3,5 anys i analitzem l'evolució de la seva competència, tal i com s'evidencia en els textos.Interpretamos AICLE como educación bilingüe en la medida en que contribuye a crear bilingües; y nos interesa el comportamiento de los bilingües emergentes. Nos adherimos también a la idea de repertorios lingüísticos holísticos en vez de lenguas separables. En este estudio seguimos en parte la investigación realizada por Celaya (2008) y Agustín-Llach (2009), con el fin de explorar ejemplos de translanguaging (acuñado en algunos casos como el "translenguar") en la producción escrita del alumnado AICLE. Nos centramos en tres categorías de lenguaje infundido por la L1: préstamo, traducción y extranjerización. Si bien se han considerado anteriormente como errores, sugerimos que sería más útil que el profesorado las tratase como estrategias comunicativas que surgen de manera natural, es decir, como manifestación del bilingüismo emergente del alumnado. Comparamos dos bases de datos de producción escrita del alumnado recogida en un intervalo de 3,5 años y analizamos la evolución de su competencia, tal y como se evidencia en sus textos