Bridging the Data Talent Gap: Positioning the iSchool as an Agent for Change

This paper examines the role, functions and value of the “iSchool” as an agent of change in the data informatics and data curation arena. A brief background to the iSchool movement is given followed by a brief review of the data decade, which highlights key data trends from the iSchool perspective: open data and open science, big data and disciplinary data diversity. The growing emphasis on the shortage of data talent is noted and a family of data science roles identified. The paper moves on to describe three primary functions of iSchools: education, research intelligence and professional practice, which form the foundations of a new Capability Ramp Model. The model is illustrated by mini-case studies from the School of Information Sciences, University of Pittsburgh: the immersive (laboratory-based) component of two new Research Data Management and Research Data Infrastructures graduate courses, a new practice partnership with the University Library System centred on RDM, and the mapping of disciplinary data practice using the Community Capability Model Profile Tool. The paper closes with a look to the future and, based on the assertion that data is mission-critical for iSchools, some steps are proposed for the next data decade: moving data education programs into the mainstream core curriculum, adopting a translational data science perspective and strengthening engagement with the Research Data Alliance.</jats:p

Research Review

Given the commonly-cited research-practice gaps and nascent status of the dissemination and implementation (DI) field as it relates to psychological science, a multidisciplinary synthesis of the literature relating to DI efforts is an important addition. This is particularly true given that one prominent criticism of the DI field is that efforts to disseminate and implement evidence-based practice (EBP) lack their own empirical foundation

Learning by Teaching about RDM: An Active Learning Model for Internal Library Education

This paper reports on the design, delivery and assessment of a model for internal library education around research data management (RDM). Conducted at the University of Pittsburgh Library System (ULS), the exercise and resultant instructional session employed an active learning approach, in which a group of librarians and archivists explored data issues and conventions in a discipline of their own selection and presented their findings to an audience of library colleagues. In this paper, we put forth an adaptable active learning model for internal RDM education and offer guidance for its implementation by peer libraries that are similarly building internal capacity for the design and delivery of RDM services that are responsive to disciplinary needs.

Learning by Teaching about RDM: An Active Learning Model for Internal Library Education

This conference presentation reported on the design, delivery, and assessment of a model for internal library education around research data management (RDM). Conducted at the University of Pittsburgh Library System (ULS), the exercise and resultant instructional session employed an active learning approach, in which a group of librarians and archivists explored data issues and conventions in a discipline of their own selection and presented their findings to an audience of library colleagues. In this presentation, we put forth an adaptable active learning model for internal RDM education and offer guidance for its implementation by peer libraries that are similarly building internal capacity for the design and delivery of RDM services that are responsive to disciplinary needs

Combining observations and numerical model results to improve estimates of hypoxic volume within the Chesapeake Bay, USA

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 118 (2013): 4924–4944, doi:10.1002/jgrc.20331.The overall size of the “dead zone” within the main stem of the Chesapeake Bay and its tidal tributaries is quantified by the hypoxic volume (HV), the volume of water with dissolved oxygen (DO) less than 2 mg/L. To improve estimates of HV, DO was subsampled from the output of 3-D model hindcasts at times/locations matching the set of 2004–2005 stations monitored by the Chesapeake Bay Program. The resulting station profiles were interpolated to produce bay-wide estimates of HV in a manner consistent with nonsynoptic, cruise-based estimates. Interpolations of the same stations sampled synoptically, as well as multiple other combinations of station profiles, were examined in order to quantify uncertainties associated with interpolating HV from observed profiles. The potential uncertainty in summer HV estimates resulting from profiles being collected over 2 weeks rather than synoptically averaged ∼5 km3. This is larger than that due to sampling at discrete stations and interpolating/extrapolating to the entire Chesapeake Bay (2.4 km3). As a result, sampling fewer, selected stations over a shorter time period is likely to reduce uncertainties associated with interpolating HV from observed profiles. A function was derived that when applied to a subset of 13 stations, significantly improved estimates of HV. Finally, multiple metrics for quantifying bay-wide hypoxia were examined, and cumulative hypoxic volume was determined to be particularly useful, as a result of its insensitivity to temporal errors and climate change. A final product of this analysis is a nearly three-decade time series of improved estimates of HV for Chesapeake Bay.Funding for this study was provided by the IOOS COMT Program through NOAA grants NA10NOS0120063 and NA11NOS0120141. Additional funding was provided by NSF grant OCE-1061564

Intentional research design in implementation science: implications for the use of nomothetic and idiographic assessment

The advancement of implementation science is dependent on identifying assessment strategies that can address implementation and clinical outcome variables in ways that are valid, relevant to stakeholders, and scalable. This paper presents a measurement agenda for implementation science that integrates the previously disparate assessment traditions of idiographic and nomothetic approaches. Although idiographic and nomothetic approaches are both used in implementation science, a review of the literature on this topic suggests that their selection can be indiscriminate, driven by convenience, and not explicitly tied to research study design. As a result, they are not typically combined deliberately or effectively. Thoughtful integration may simultaneously enhance both the rigor and relevance of assessments across multiple levels within health service systems. Background on nomothetic and idiographic assessment is provided as well as their potential to support research in implementation science. Drawing from an existing framework, seven structures (of various sequencing and weighting options) and five functions (Convergence, Complementarity, Expansion, Development, Sampling) for integrating conceptually distinct research methods are articulated as they apply to the deliberate, design-driven integration of nomothetic and idiographic assessment approaches. Specific examples and practical guidance are provided to inform research consistent with this framework. Selection and integration of idiographic and nomothetic assessments for implementation science research designs can be improved. The current paper argues for the deliberate application of a clear framework to improve the rigor and relevance of contemporary assessment strategies

A Mixed Methods Study of Individual and Organizational Factors that Affect Implementation of Interventions for Children with Autism in Public Schools

Background: The significant lifelong impairments associated with autism spectrum disorder (ASD), combined with the growing number of children diagnosed with ASD, have created urgency in improving school-based quality of care. Although many interventions have shown efficacy in university-based research, few have been effectively implemented and sustained in schools, the primary setting in which children with ASD receive services. Individual- and organizational-level factors have been shown to predict the implementation of evidence-based interventions (EBIs) for the prevention and treatment of other mental disorders in schools, and may be potential targets for implementation strategies in the successful use of autism EBIs in schools. The purpose of this study is to examine the individual- and organizational-level factors associated with the implementation of EBIs for children with ASD in public schools. Methods: We will apply the Domitrovich and colleagues (2008) framework that examines the influence of contextual factors (i.e., individual- and organizational-level factors) on intervention implementation in schools. We utilize mixed methods to quantitatively test whether the factors identified in the Domitrovich and colleagues (2008) framework are associated with the implementation of autism EBIs, and use qualitative methods to provide a more comprehensive understanding of the factors associated with successful implementation and sustainment of these interventions with the goal of tailoring implementation strategies. Discussion: The results of this study will provide an in-depth understanding of individual- and organizational-level factors that influence the successful implementation of EBIs for children with ASD in public schools. These data will inform potential implementation targets and tailoring of strategies that will help schools overcome barriers to implementation and ultimately improve the services and outcomes for children with ASD