Talk about your business risks...

Put not your faith in cloud services. This is why.

Telling lies with data.

The journal Cell Cycle is expressing a “note” of concern about a 2012 paper by a former researcher at the University of Minnesota, who has claimed that her mentor at the institution was…

"To examine a microcosm of the academic experience with data reproducibility, we surveyed the faculty and trainees at MD Anderson Cancer Center using an anonymous computerized questionnaire; we sought to ascertain the frequency and potential causes of non-reproducible data."

The famous "only 6% of cancer research results hold up under scrutiny" paper.

"Following inquiries, it turns out that the raw data are no longer available having been lost as a result of computer failure." GOOD JOB, GUYS.

Shows the difference between data and graphs.

The eventual replication of the data from the Reinhart-Rogoff paper on 90% debt/GDP threshold has sparked vibrant discussion on the impact of error-ridden research on austerity policies around the …

Another lost laptop.

Objectives: (1) to identify common errors in data organization and metadata completeness that would preclude a “reader” from being able to interpret and re-use the data for a new purpose; and (2) to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used. Methods: We used directed qualitative content analysis to assess and categorize data and metadata errors identified by peer reviewers of data papers published in the Ecological Society of America’s (ESA) Ecological Archives. Descriptive statistics provided the relative frequency of the errors identified during the peer review process. Results: There were seven overarching error categories: Collection & Organization, Assure, Description, Preserve, Discover, Integrate, and Analyze/Visualize. These categories represent errors researchers regularly make at each stage of the Data Life Cycle. Collection & Organization and Description errors were some of the most common errors, both of which occurred in over 90% of the papers.  Conclusions: Publishing data for sharing and reuse is error prone, and each stage of the Data Life Cycle presents opportunities for mistakes. The most common errors occurred when the researcher did not provide adequate metadata to enable others to interpret and potentially re-use the data. Fortunately, there are ways to minimize these mistakes through carefully recording all details about study context, data collection, QA/ QC, and analytical procedures from the beginning of a research project and then including this descriptive information in the metadata.

Background When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names. Results A little detective work traced the problem to default date format conversions and floating-point format conversions in the very useful Excel program package. The date conversions affect at least 30 gene names; the floating-point conversions affect at least 2,000 if Riken identifiers are included. These conversions are irreversible; the original gene names cannot be recovered. Conclusions Users of Excel for analyses involving gene names should be aware of this problem, which can cause genes, including medically important ones, to be lost from view and which has contaminated even carefully curated public databases. We provide work-arounds and scripts for circumventing the problem.

Coding errors in a database maintained by Pfizer have led authors to retract two heart biomarker papers in the Journal of the American College of Cardiology. The two notices, for “Prediction …

A paper used to justify austerity economics appears to contain an Excel error.

News, Updates, & Everything In Between!

The bizarre and complicated story is a cautionary tale about what we know and don’t know in the 21st century.

Rick's Rant for February 26th, 2013.

Scientists recant in ethics row over publishing DNA genome of research's unwitting heroine

The academic community I’m most heavily involved in – Digital Humanities – are fairly invested in twitter. At all time...