This post is another in my ongoing series of “basic questions about open data”, which includes “What is a Dataset?” and “What does a dataset contain?”. In this p…

Traditionally, there has been a blithe assumption that more innovation occurs when patents are granted than when they are not.  But as...

Another lost laptop.

well it's about damn time.

This blog post is cross-posted from the Open Economics Blog. Another economics scandal made the news last week. Harvard Kennedy School professor Carmen Reinhart and Harvard University profes…

We are excited to announce the official launch of Crowdcrafting.org, an open source software platform – powered by our Pybossa technology – for developing and sharing projects that rely…

Excuses for not opening data.

Concerns about opening up data, and responses which have proved effective (or “How to make friends and get them to give you their data”)  Christopher Gutteridge, University of Southampton Alexander Dutton, University of Oxford  This document is inspired by the open data excuses bingo card.  Some...

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.

People need more than free pizza to solve problems.

"The [new peer review] checklist also consolidates several existing policies about data deposition and data presentation." "To further increase transparency, we now also encourage authors to provide, in tabular form, the data underlying the graphical representations used in figures. This is in addition to our well-established data-deposition policy for specific types of experiments and large datasets."

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 …

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

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