Dataset Information

Setting a Standard for Chemistry Education in the Next Generation: A Retrosynthetic Analysis.

ABSTRACT: A diverse and highly qualified chemistry teaching workforce is critical for preparing equally diverse, qualified STEM professionals. Here, we analyze National Center for Education Statistics (NCES) Schools and Staffing Survey (SASS) data to provide a demographic comparison of the U.S. secondary chemistry teaching population in high-needs and non-high-needs public schools as well as private schools during the 2011-2012 academic year. Our analysis reveals that the chemistry teaching workforce is predominantly white and significantly lacks in-field degrees or certification across school types, though high-needs and private schools are most affected by this lack of teacher qualification. Given these results, we attempt to retrosynthetically identify the pathway yielding a qualified chemistry teaching workforce to draw attention to the various steps in this scheme where reform efforts on the part of individual faculty, academic institutions, and organizations can be concentrated.

SUBMITTER: Rushton GT

PROVIDER: S-EPMC5126716 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Setting a Standard for Chemistry Education in the Next Generation: A Retrosynthetic Analysis.

Rushton Gregory T GT Dewar Andrew A Ray Herman E HE Criswell Brett A BA Shah Lisa L

ACS central science 20161017 11

A diverse and highly qualified chemistry teaching workforce is critical for preparing equally diverse, qualified STEM professionals. Here, we analyze National Center for Education Statistics (NCES) Schools and Staffing Survey (SASS) data to provide a demographic comparison of the U.S. secondary chemistry teaching population in high-needs and non-high-needs public schools as well as private schools during the 2011-2012 academic year. Our analysis reveals that the chemistry teaching workforce is p ...[more]

PMID: 27924311

Similar Datasets

Project description:BackgroundNext-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards.MethodsWe developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1/BRCA2 gene testing pipeline.ResultsCAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92 % had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46 %) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1/BRCA2 gene mutations from a clinical pipeline gave 100 % concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools.ConclusionsCAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized clinical sequencing nomenclature.

Dataset Information

Setting a Standard for Chemistry Education in the Next Generation: A Retrosynthetic Analysis.

Publications

Setting a Standard for Chemistry Education in the Next Generation: A Retrosynthetic Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets