Dataset Information

Gene name errors: Lessons not learned.

ABSTRACT: Erroneous conversion of gene names into other dates and other data types has been a frustration for computational biologists for years. We hypothesized that such errors in supplementary files might diminish after a report in 2016 highlighting the extent of the problem. To assess this, we performed a scan of supplementary files published in PubMed Central from 2014 to 2020. Overall, gene name errors continued to accumulate unabated in the period after 2016. An improved scanning software we developed identified gene name errors in 30.9% (3,436/11,117) of articles with supplementary Excel gene lists; a figure significantly higher than previously estimated. This is due to gene names being converted not just to dates and floating-point numbers, but also to internal date format (five-digit numbers). These findings further reinforce that spreadsheets are ill-suited to use with large genomic data.

SUBMITTER: Abeysooriya M

PROVIDER: S-EPMC8357140 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Gene name errors: Lessons not learned.

Abeysooriya Mandhri M Soria Megan M Kasu Mary Sravya MS Ziemann Mark M

PLoS computational biology 20210730 7

Erroneous conversion of gene names into other dates and other data types has been a frustration for computational biologists for years. We hypothesized that such errors in supplementary files might diminish after a report in 2016 highlighting the extent of the problem. To assess this, we performed a scan of supplementary files published in PubMed Central from 2014 to 2020. Overall, gene name errors continued to accumulate unabated in the period after 2016. An improved scanning software we develo ...[more]

PMID: 34329294

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Project description:Challenges in measuring early childhood development (ECD) at scale have been documented, yet little is known about the specific difficulties related to questionnaire design and question interpretation. The purpose of this paper is to discuss the challenges of measuring ECD at scale in the context of household surveys and to show how to overcome them. The paper uses examples from the cognitive interviewing exercises that were conducted as part of the methodological work to develop a measure of ECD outcomes, the ECDI2030. It describes the methodological work carried out to inform the selection and improvement of question items and survey implementation tools as a fundamental step to reduce and mitigate systematic measurement error and improve data quality. The project consisted of a total of five rounds of testing, comprising 191 one-on-one, in-depth cognitive interviews across six countries (Bulgaria, India, Jamaica, Mexico, Uganda, and the USA). Qualitative data analysis methods were used to determine matches and mismatches between intention of items and false positives or false negative answers among subgroups of respondents. Key themes emerged that could potentially lead to systematic measurement error in population-based surveys on ECD: (1) willingness of child to perform task versus ability of child to perform task; (2) performing task versus performing task correctly; (3) identifying letters or numbers versus recognizing letters or numbers; (4) consistently performing task versus correctly performing task; (5) applicability of skills being asked versus observability of skills being asked; and (6) language production versus language comprehension. Through an iterative process of testing and subsequent revision, improvements were made to item wording, response options, and interviewer training instructions. Given the difficulties inherent in population-level data collection in the context of global monitoring, this study's findings confirm the importance of cognitive testing as a crucial step in careful, culturally relevant, and sensitive questionnaire design and as a means to reduce response bias in cross-cultural contexts.

Dataset Information

Gene name errors: Lessons not learned.

Publications

Gene name errors: Lessons not learned.

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