The purpose of this resource is to introduce the process of benchmarking in farming. It is a very basic tutorial aimed at familiarizing farmers with the practice of benchmarking in farm management. This resource hope to encourage learners to research further learning resources and opportunities. The content of this resource is derived from:

Kahan, D. (2013). Farm business analysis using benchmarking. Rome: Food and Agriculture Organization of the United Nations.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Much like the organisms that flood its instruments the microbiome research community is thriving. But researchers from the UK’s National Institute for Biological Standards and Control (NIBSC) say that it could be doing even better. They’ve developed the first reference reagents for microbiome DNA analysis, Gut-Mix-RR and Gut-HiLo-RR. It’s a move designed to promote standardization and reproducibility across the field of microbiome research as tests revealed drastic variations across shotgun sequencing taxonomic profilers, which could alter conclusions about interactions between different microbes . If researchers can reach a consensus on acceptable levels of errors and begin using the materials in their labs the reference reagents could help standardize downstream gut microbiome analyses. A large open-invite collaborative study for multiple laboratories is slated for later in 2020..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Bacteriophages are everywhere, influencing everything from microbial evolution to biogeochemical cycling. Phages, however, remain among the least understood members of complex microbiomes. Do the tools used to identify phages introduce biases? A recent study compared ten of the most widely used bioinformatics tools designed to detect phages from metagenomics data. Overall, tool performance varied substantially in the analysis of different benchmarking datasets. For a set of artificial RefSeq contigs, PPR Meta and VirSorter2 showed the highest performance. Kraken2 showed the highest accuracy for a mock community benchmark. And generally, k-mer tools performed better than similarity- or gene-based tools. The study offers insight into the biases introduced by different tools, offers guidance into which one is best suited for different use cases, and suggests that rather than relying on any one tool, researchers may do well to combine different ones to suit their research needs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Differential abundance analysis (DAA) is a key statistical method for comparing microbiome compositions under different conditions, such as health vs. disease. However, DAA is complicated by the use of relative, rather than absolute, abundance values and by a high risk of false positives, or detection of significant effects when there aren’t any. In addition, the existing DAA tools can produce very divergent results from the same data, making it difficult to select the best tool. To provide guidance, a new study comprehensively evaluated the currently available tools with simulations based on real data. The researchers found that none of the tools were simultaneously robust, powerful, and flexible. Therefore, they concluded that none were suitable for blind application to real microbiome datasets. To build a better path forward, the researchers designed a new tool, ZicoSeq that drew on the strengths of the other available DAA methods while addressing their major limitations..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.