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Last fall, Cornell University introduced a data science course where students learned to deal with ethical challenges — such as biased data sets that include too few lower-income households to be representative of the general population.
INTEL, for instance, recently introduced a "data economy" project, intended to encourage companies to think of consumers as participants in the information economy, and not just as data-harvesting opportunities.
So we've recently introduced a data warehouse capability.
Recently, we introduced a data logger with 1 kHz sampling, which turned out to be sufficient for frequencies lower than 40 Hz, for example.
The fact that only one observer who was not blinded to the hypothesis collected the data for the study could have introduced a data collection bias.
We generated more that 3.6 million sequence reads of average length 250 bp for the MAQC A and B samples and introduced a data analysis pipeline for translating cDNA read counts into gene expression levels.
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To handle these large data sets in IGV, we introduced a data-loading scheme that includes indexing of data files as well as on-demand loading.
We introduce a data poisoning attack on collaborative filtering systems.
Next, we introduce a data structure to describe time-varying neural reconstructions.
To achieve these goals, Spark introduces a data abstraction called resilient distributed datasets (RDDs).
We also introduce a data caching mechanism at cloudlets to further improve the overall MCC performance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com