We have recently upgraded to Sonar 5.3 from 4.4.1 and seen duplicate key issue with one of our projects. I checked the collation and found database server and database to be case insensitive and column to be case sensitive. Should we change collation on the database to "Latin1_General_CS_AS" ? Changing the collation on the server is bit difficult but we can try with database.
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Individual audio or MIDI clips can be exported to disk in the Live Clip format for easy retrieval and reuse in any project. Audio clips only contain references to samples on disk (rather than the audio data itself), so they are very small, which makes it easy to develop and maintain your own collection.
Live makes it easy to merge sets, which can come in handy when combining work from different versions or pieces. To add all tracks (except the return tracks) from one Live Set into another, drag the set from the browser into the current set, and drop it onto any track title bar or into the drop area next to or below the tracks. The tracks from the dropped set will be completely reconstructed, including their clips in the Session and Arrangement View, their devices, and their automation.
Review authors often have different backgrounds and level of systematic review experience. Using a data collection form ensures some consistency in the process of data extraction, and is necessary for comparing data extracted in duplicate. The completed data collection forms should be available to the CRG on request. Piloting the form within the review team is highly desirable. At minimum, the data collection form (or a very close variant of it) must have been assessed for usability.
Regardless of whether data are collected using a paper or electronic form, or a data system, the key to successful data collection is to construct easy-to-use forms and collect sufficient and unambiguous data that faithfully represent the source in a structured and organized manner (Li et al 2015). In most cases, a document format should be developed for the form before building an electronic form or a data system. This can be distributed to others, including programmers and data analysts, and as a guide for creating an electronic form and any guidance or codebook to be used by data extractors. Review authors also should consider compatibility of any electronic form or data system with analytical software, as well as mechanisms for recording, assessing and correcting data entry errors.
Evidence in support of duplicate data extraction comes from several indirect sources. One study observed that independent data extraction by two authors resulted in fewer errors than data extraction by a single author followed by verification by a second (Buscemi et al 2006). A high prevalence of data extraction errors (errors in 20 out of 34 reviews) has been observed (Jones et al 2005). A further study of data extraction to compute standardized mean differences found that a minimum of seven out of 27 reviews had substantial errors (Gøtzsche et al 2007).
It is preferable to identify potential problems before, rather than after, publication of the systematic review, so that readers are not misled. However, empirical evidence indicates that the extent to which systematic review authors explore misconduct varies widely (Elia et al 2016). Text-matching software and systems such as CrossCheck may be helpful for detecting plagiarism, but they can detect only matching text, so data tables or figures need to be inspected by hand or using other systems (e.g. to detect image manipulation). Lists of data such as in a meta-analysis can be a useful means of detecting duplicated studies. Furthermore, examination of baseline data can lead to suspicions of misconduct for an individual randomized trial (Carlisle et al 2015). For example, Al-Marzouki and colleagues concluded that a trial report was fabricated or falsified on the basis of highly unlikely baseline differences between two randomized groups (Al-Marzouki et al 2005).
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A duplicate photo finder or duplicate photo remover is a piece of software to automate the process of identifying and deleting duplicate photos. We tested a dozen of well-known duplicate photo finders for Windows and picked the best ones. They vary in features, performance, ease of use, cost, etc. Below are the reviews. #1 Cisdem Duplicate Finder.
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Duplicate File Finder lets you get more free disk space by removing unnecessary identical or similar files and folders from your Mac. It works with a variety of file formats and has a really fast scanning algorithm to find matches.FixthephotoDuplicate File FinderGet rid of duplicate files and free up your disk from useless content absolutely for free!
Stripe offers an easy way to set up a billing portal so that your customer can manage their subscription, payment methods, and view their billing history. You can redirect your users to the billing portal by invoking the redirectToBillingPortal method on the billable model from a controller or route:
By default, Stripe Checkout does not allow user redeemable promotion codes. Luckily, there's an easy way to enable these for your Checkout page. To do so, you may invoke the allowPromotionCodes method:
Media Deduper can differentiate between 1.) media items that are duplicates because the media files they link to have the same data and 2.) those that actually point to the same data file, which can happen with a plugin like WP Job Manager or Duplicate Post.
Instead, we recommend using the Smart Delete action (which is also found in the Bulk Actions menu). Smart Delete will delete the selected items one by one, and refuse to delete an item if it has no remaining duplicates. For example, if you have three copies of an image, and you select all three and choose Smart Delete, two copies will be deleted and the third will be skipped.
We provide a container that wraps everything required to run a BUSCO analysis.This streamlines the setup and installation and makes it easy to track all software versions used in the analyses (v5.4.4_cv1 identifies all components at once). It guarantees that only dependency versions compatible with BUSCO are used.
BUSCO attempts to provide a quantitative assessment of the completeness in terms of expected gene content of a genome assembly, transcriptome, or annotated gene set. The results are simplified into categories of Complete and single-copy, Complete and duplicated, Fragmented, or Missing BUSCOs.
BUSCO completeness results make sense only in the context of the biology of your organism. You have to understand whether missing or duplicated genes are of biological or technical origin. For instance, a high level of duplication may be explained by a recent whole duplication event (biological) or a chimeric assembly of haplotypes (technical). Transcriptomes and protein sets that are not filtered for isoforms will lead to a high proportion of duplicates. Therefore you should filter them before a BUSCO analysis. Finally, focusing on specific tissues or specific life stages and conditions in a transcriptomic experiment is unlikely to produce a BUSCO-complete transcriptome. In this case, consistency across your samples is what you will be aiming for.
If found to be complete, whether single-copy or duplicated, the BUSCO matches have scored within the expected range of scores and within the expected range of length alignments to the BUSCO profile. If in fact an ortholog is not present in the input dataset, or the ortholog is only partially present (highly fragmented), and a high-identity full-length homolog is present, it is possible that this homolog could be mistakenly identified as the complete BUSCO. The score thresholds are optimised to minimise this possibility, but it can still occur. 2ff7e9595c
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