Dataset Information

Implementation and assessment of an end-to-end Open Science & Data Collaborations program.

ABSTRACT: As research becomes more interdisciplinary, fast-paced, data-intensive, and collaborative, there is an increasing need to share data and other research products in accordance with Open Science principles. In response to this need, we created an Open Science & Data Collaborations (OSDC) program at the Carnegie Mellon University Libraries that provides Open Science tools, training, collaboration opportunities, and community-building events to support Open Research and Open Science adoption. This program presents a unique end-to-end model for Open Science programs because it extends open science support beyond open repositories and open access publishing to the entire research lifecycle. We developed a logic model and a preliminary assessment metrics framework to evaluate the impact of the program activities based on existing data collected through event and workshop registrations and platform usage. The combination of these evaluation instruments has provided initial insight into our service productivity and impact. It will further help to answer more in-depth questions regarding the program impact, launch targeted surveys, and identify priority service areas and interesting Open Science projects.

SUBMITTER: Wang H

PROVIDER: S-EPMC9748376 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implementation and assessment of an end-to-end Open Science & Data Collaborations program.

Wang Huajin H Gainey Melanie M Campbell Patrick P Young Sarah S Behrman Katie K

F1000Research 20220505

As research becomes more interdisciplinary, fast-paced, data-intensive, and collaborative, there is an increasing need to share data and other research products in accordance with Open Science principles. In response to this need, we created an Open Science & Data Collaborations (OSDC) program at the Carnegie Mellon University Libraries that provides Open Science tools, training, collaboration opportunities, and community-building events to support Open Research and Open Science adoption. This p ...[more]

PMID: 36545378

Similar Datasets

Project description:BackgroundTo meet the growing demand for implementation science expertise, building capacity is a priority. Various training opportunities have emerged to meet this need. To ensure rigor and achievement of specific implementation science competencies, it is critical to systematically evaluate training programs.MethodsThe Penn Implementation Science Institute (PennISI) offers 4 days (20 h) of virtual synchronous training on foundational and advanced topics in implementation science. Through a pre-post design, this study evaluated the sixth PennISI, delivered in 2022. Surveys measures included 43 implementation science training evaluation competencies grouped into four thematic domains (e.g., items related to implementation science study design grouped into the "design, background, and rationale" competency category), course-specific evaluation criteria, and open-ended questions to evaluate change in knowledge and suggestions for improving future institutes. Mean composite scores were created for each of the competency themes. Descriptive statistics and thematic analysis were completed.ResultsOne hundred four (95.41% response rate) and 55 (50.46% response rate) participants completed the pre-survey and post-survey, respectively. Participants included a diverse cohort of individuals primarily affiliated with US-based academic institutions and self-reported as having novice or beginner-level knowledge of implementation science at baseline (81.73%). In the pre-survey, all mean composite scores for implementation science competencies were below one (i.e., beginner-level). Participants reported high value from the PennISI across standard course evaluation criteria (e.g., mean score of 3.77/4.00 for overall quality of course). Scores for all competency domains increased to a score between beginner-level and intermediate-level following training. In both the pre-survey and post-survey, competencies related to "definition, background, and rationale" had the highest mean composite score, whereas competencies related to "design and analysis" received the lowest score. Qualitative themes offered impressions of the PennISI, didactic content, PennISI structure, and suggestions for improvement. Prior experience with or knowledge of implementation science influenced many themes.ConclusionsThis evaluation highlights the strengths of an established implementation science institute, which can serve as a model for brief, virtual training programs. Findings provide insight for improving future program efforts to meet the needs of the heterogenous implementation science community (e.g., different disciplines and levels of implementation science knowledge). This study contributes to ensuring rigorous implementation science capacity building through the evaluation of programs.

Project description:BackgroundIn Uganda and other resource-poor countries, relevant research findings face a tortuous path to translation into policy and routine practice. Implementation science (ImSc) research could facilitate faster translation. Presently it is unclear what ImSc research capacity and possible training needs exist among Ugandan researchers. To assess both components, we interviewed potential trainees in Kampala, Uganda.MethodsWe used a cross-sectional design to survey potential ImSc trainees who had some research training and involvement in generating or utilizing research. Using a questionnaire, we documented eligibility for ImSc training, knowledge and interest in training, existing self-assessed confidence in initiating clinical research (SCICR) and self-assessed confidence in initiating ImSc research (SCIIR), availability for training and preferred modes of training. We developed scores from the Likert scales and used descriptive statistics, logistic regression and ordinal logistic regression to evaluate predictors of SCIIR.ResultsBetween November 2016 and April 2017, we interviewed 190 participants; 60% were men, with a median age of 37 years. Among participants, 33% comprised faculty, 37% were graduate students and 30% were project staff. The majority of respondents knew about ImSc (73%) and were research-trained (80%). Only 9% reported any ImSc-related training. Previous ImSc training was associated with higher odds of a SCIIR score ≥ 75th percentile. Previous ImSc training compared to not having any training was associated with higher odds of reporting abilities in behaviour change theory integration (OR: 3.3, 95% CI: 1.3-8.5, p = 0.01) and framework use in intervention design and implementation (OR: 2.9, 95% CI: 1.1-7.4, p = 0.03), accounting for age, sex and current employment. In addition, 53% of participants preferred in-person (face-to-face) short ImSc courses compared to a year-long training, while 33% preferred online courses. Participants reported median availability of 6 hours per week (IQR: 4, 10) for training.ConclusionMost participants had some understanding of ImSc research, had research training and were interested in ImSc training. Those with previous ImSc training had better skills and SCIIR, compared to those without previous training. A hybrid approach with modular face-to-face training and online sessions would suit the preferences of most potential trainees.

Project description:BackgroundThe purpose of the Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) Program, a Cancer Moonshot℠ Initiative, is to support research to build the evidence base on multilevel interventions that increase rates of colorectal cancer screening, follow-up, and referral to care to address disparities in colorectal cancer screening. The National Cancer Institute funded eight Research Projects to implement multilevel interventions to improve colorectal cancer screening among communities who traditionally have been medically underserved. To analyze the impact of ACCSIS across Research Projects, the consortium developed a set of common data elements. The purpose of this paper is to describe the process of developing the common data elements to facilitate analysis of ACCSIS data as well as support and inform implementation science research studies.MethodsThe ACCSIS Data, Design, and Analysis Work Group was tasked with designing common data elements through a review of existing data collection instruments, examination of data elements proposed by the ACCSIS Research Projects, and deliberations on the data required to compare across the Research Projects. ACCSIS Consortium members drafted, revised, and finalized a common data elements document consisting of variables to collect and surveys to administer to evaluate ACCSIS implementation activities and outcomes in a standardized manner across the Research Projects.ResultsThe ACCSIS Consortium decided to collect the following categories of common data elements: characteristics across multiple levels of the multicomponent interventions, implementation climate, and determinants; interventions and strategies; implementation outcome constructs and definitions; colorectal cancer screening episodes (screening, diagnostic testing follow-up, cancer detection, and cancer treatment); and cost measures. To assess implementation climate, the consortium prioritized constructs from five domains of the Consolidated Framework for Implementation Research.ConclusionsThe ACCSIS common data elements offer a set of harmonization data for future implementation efforts. The consortium is conducting a systematic assessment using both quantitative and qualitative approaches to assess data quality and approaches to improve and sustain data collection. Lessons learned from these ongoing activities will offer additional insights to tailor the ACCSIS common data elements and support efforts to increase colorectal cancer screening for populations experiencing disparities.

Dataset Information

Implementation and assessment of an end-to-end Open Science & Data Collaborations program.

Publications

Implementation and assessment of an end-to-end Open Science & Data Collaborations program.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets