Edit Prep

Edit Prep

Teachers can test their reading and writing students’ skills with Edit Prep. The program creates worksheets that students can proofread for correct punctuation and grammar usage, or read for contextual clues to test their reading comprehension skills.

We tried each of Edit Prep’s three sections: Proofreading, Comprehension, and CLOZE Worksheet, which deals with finding contextual clues in passages of text. The program has a selection of sample text from well-known works of literature from which you can generate worksheets, or users can import passages from sources outside the program. We think teachers will enjoy this option, because they can use passages from books or other types of literature that they are currently teaching in their classrooms. Edit Prep comes with some clip art and some limited formatting options, but users may prefer formatting them in word-processing programs with which they are familiar for greater control. Overall, we found Edit Prep easy to use, but Help files are available within each section of the program should you need them.

The program includes a 14-day trial period. If you purchase the full version of the program, you can copy the worksheets into the word processing program of your choice with one click. Edit Prep’s only drawback is that it does not install or uninstall cleanly. When installing the program, Desktop and Quick Launch icons and bundled applications were added without asking. Still, we recommend this program for teachers, as well as students wanting to improve their grammar skills.

Tableau Prep changes the way traditional data prep is performed in an organization. By providing a visual and direct way to combine, shape and clean data, Tableau Prep makes it easier for analysts and business users to start their analysis, faster.

Tableau Prep is comprised of two products: Tableau Prep Builder for building your data flows, and Tableau Prep Conductor for scheduling, monitoring and managing flows across the organization.

RNA editing is a process that converts specific cytidines to uridines in mitochondrial and chloroplast transcripts of land plants. This process tends to increase protein conservation across species by “correcting” codons that specify unconserved amino acids. Exploiting this principle, the PREP suite of programs computationally predicts C to U edit sites in an organellar protein-coding gene by comparing its predicted protein sequence to homologous proteins from other plants.

The PREP suite consists of PREP-Mt (for mitochondrial genes), PREP-Cp (for chloroplast genes), and PREP-Aln (for user-submitted alignments). The PREP method has been shown to be both fast and accurate for most seed plant genes and genera. A description of PREP-Aln, PREP-Cp, and PREP-Mt can be found in the references below. Please also cite the appropriate reference if you use any of the PREP programs in your research.Mower, J. P. (2005). PREP-Mt: Predictive RNA editor for plant mitochondrial genes. BMC Bioinformatics, 6:96. [Article]

Mower, J. P. (2009). The PREP Suite: Predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments. Nucl. Acids Res., 37:W253-W259. [Article]

Tableau Prep provides various cleaning operations that you can use out of the box to clean and shape your data. Cleaning up dirty data makes it easier to combine and analyze your data or makes it easier for others to understand your data when sharing your data sets.

You can also clean your data using a pivot step or a script step to apply R or Python scripts to your flow. Script steps are not supported in Tableau Online. For more information, see Pivot Your Data(Link opens in a new window) or Use R and Python scripts in your flow(Link opens in a new window).

Create an editable hosted feature layer that mobile workers use to collect assets and observations. Editable feature layers can be created in ArcGIS Online, ArcGIS Enterprise, or ArcGIS Pro. They can be created by using templates, importing existing data from your organization, or a combination of both.

First, consider how you will use existing data. If your collection project is about inspections (either ground truthing or updating assets or observations based on current status), your mobile workers need to use existing data while in the field. Do you want to define the structure of the new layer yourself (specifying the fields) and import existing data to that structure or is the structure of your existing data sufficient for your collection project? To define the structure, create a layer and load the existing data into it. To use the structure of the existing data, create the layer based on that existing data.