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Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012–2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets.

[This corrects the article DOI: 10.1371/journal.pone.0302426.].

Rapid detection of Klebsiella pneumoniae carbapenemase (KPC) in the Klebsiella species is desirable. The MALDI Biotyper® MBT Subtyping Module (Bruker Daltonics) uses an algorithm that detects a peak at ~11,109 m/z corresponding to a protein encoded by the p019 gene to detect KPC simultaneously with organism identification by a matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-ToF MS). Here, the subtyping module was evaluated using 795 clinical Klebsiella isolates, with whole genome sequences used to assess for blaKPC and p019. For the isolates identified as KPC positive by sequencing, the overall sensitivity of the MALDI-ToF MS subtyping module was 239/574 (42%) with 100% specificity. For the isolates harboring p019, the subtyping module showed a sensitivity of 97% (239/246) and a specificity of 100%. The subtyping module had poor sensitivity for the detection of blaKPC-positive Klebsiella isolates, albeit exhibiting excellent specificity. The poor sensitivity was a result of p019 being present in only 43% of the blaKPC-positive Klebsiella isolates.

Abstract Background Ceftriaxone-resistant (CRO-R) Escherichia coli bloodstream infections (BSIs) are common. Methods This is a prospective cohort of patients with E coli BSI at 14 United States hospitals between November 2020 and April 2021. For each patient with a CRO-R E coli BSI enrolled, the next consecutive patient with a ceftriaxone-susceptible (CRO-S) E coli BSI was included. Primary outcome was desirability of outcome ranking (DOOR) at day 30, with 50% probability of worse outcomes in the CRO-R group as the null hypothesis. Inverse probability weighting (IPW) was used to reduce confounding. Results Notable differences between patients infected with CRO-R and CRO-S E coli BSI included the proportion with Pitt bacteremia score ≥4 (23% vs 15%, P = .079) and the median time to active antibiotic therapy (12 hours [interquartile range {IQR}, 1–35 hours] vs 1 hour [IQR, 0–6 hours]; P < .001). Unadjusted DOOR analyses indicated a 58% probability (95% confidence interval [CI], 52%–63%) for a worse clinical outcome in CRO-R versus CRO-S BSI. In the IPW-adjusted cohort, no difference was observed (54% [95% CI, 47%–61%]). Secondary outcomes included unadjusted and adjusted differences in the proportion of 30-day mortality between CRO-R and CRO-S BSIs (−5.3% [95% CI, −10.3% to −.4%] and −1.8 [95% CI, −6.7% to 3.2%], respectively), postculture median length of stay (8 days [IQR, 5–13 days] vs 6 days [IQR, 4–9 days]; P < .001), and incident admission to a long-term care facility (22% vs 12%, P = .045). Conclusions Patients with CRO-R E coli BSI generally have poorer outcomes compared to patients infected with CRO-S E coli BSI, even after adjusting for important confounders.

A data monitoring committee (DMC) can have an extremely challenging job. Stop a trial too soon, and results are inconclusive and the trial fails to obtain answers to important questions that could inform future clinical practice. Stop a trial too late, and trial participants are exposed to potentially harmful or ineffective interventions longer than necessary. Securing convincing and conclusive evidence and the ethical responsibility to current and future patients are weighed carefully during DMC deliberations. The ability to interpret complex information, and appreciation of issues affecting scientific integrity, are critical for the DMC to protect trial participants and public trust. Challenges faced by and issues of prudence faced by DMCs are discussed including interim analysis issues, assessing the totality of information with statistical boundaries as guidelines, interpretation of composite and surrogate outcomes, reactions to early trends, benefit:risk assessment, landscape changes, subgroup analyses, composing information for a comprehensive understanding of patient-centric effects, and evaluating the value of additional data. Case studies illustrate how DMCs addressed the challenges.

Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012-2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets.