Introduction

Data Review

Data review can be a time consuming process, especially when done manually. This review report aims to help facilitate data review of species interaction claims made in datasets registered with Global Biotic Interactions (Poelen, Simons, and Mungall 2014). The review includes summary statistics of, and observations about, the dataset under review:

Arizona State University Hasbrouck Insect Collection https://github.com/globalbioticinteractions/asu-asuhic/archive/4ed77cb9ca8e526269d4678692e2844c950022f8.zip 2024-11-16T00:07:20.986Z 4209c270399e8d6446cc0457edb818cc5dd9a1c29862201b839710b6fbfc0ab1

For additional metadata related to this dataset, please visit https://github.com/globalbioticinteractions/asu-asuhic and inspect associated metadata files including, but not limited to, README.md, eml.xml, and/or globi.json.

Methods

The review is performed through programmatic scripts that leverage tools like Preston, Elton, Nomer combined with third-party tools like grep, mlr, tail and head.

The review process can be described in the form of the script below ¹.

# get versioned copy of the dataset (size approx. 20.3MiB) under review 
elton pull globalbioticinteractions/asu-asuhic

# generate review notes
elton review globalbioticinteractions/asu-asuhic\
 > review.tsv

# export indexed interaction records
elton interactions globalbioticinteractions/asu-asuhic\
 > interactions.tsv

# export names and align them with the Catalogue of Life using Nomer 
elton names globalbioticinteractions/asu-asuhic\
 | nomer append col\
 > name-alignment.tsv

or visually, in a process diagram.

You can find a recent copy of the full review script at check-data.sh.

Results

In the following sections, the results of the review are summarized ². Then, links to the detailed review reports are provided.

Biotic Interactions

In this review, biotic interactions (or biotic associations) are modeled as a primary (aka subject, source) organism interacting with an associate (aka object, target) organism. The dataset under review classified the primary/associate organisms with specific taxa. The primary and associate organisms The kind of interaction is documented as an interaction type.

The dataset under review, named globalbioticinteractions/asu-asuhic, is 20.3MiB in size and contains 24,931 interaction with 4 unique types of associations (e.g., interactsWith) between 578 primary taxa (e.g., Oncerus floralis) and 812 associated taxa (e.g., Allionia incarnata L. (trailing windmills) [USDA]).

An exhaustive list of indexed interaction claims can be found in csv and tsv archives. To facilitate discovery, the first 500 claims available on the html page at indexed-interactions.html are shown below.

The exhaustive list was used to create the following data summaries below.

Interaction Networks

The figures below provide a graph view on the dataset under review. The first shows a summary network on the kingdom level, and the second shows how interactions on the family level. It is important to note that both network graphs were first aligned taxonomically using the Catalogue of Life. Please refer to the original (or verbatim) taxonomic names for a more original view on the interaction data.

You can download the indexed dataset under review at indexed-interactions.csv. A tab-separated file can be found at indexed-interactions.tsv

Learn more about the structure of this download at GloBI website, by opening a GitHub issue, or by sending an email.

Another way to discover the dataset under review is by searching for it on the GloBI website.

Taxonomic Alignment

As part of the review, all names are aligned against various name catalogs (e.g., col, ncbi, discoverlife, gbif, itis, wfo, mdd, tpt, pbdb, and worms). These alignments can help review name usage or aid in selecting of a suitable taxonomic name resource.

Additional Reviews

Elton, Nomer, and other tools may have difficulties interpreting existing species interaction datasets. Or, they may misbehave, or otherwise show unexpected behavior. As part of the review process, detailed review notes are kept that document possibly misbehaving, or confused, review bots. An sample of review notes associated with this review can be found below.

In addtion, you can find the most frequently occurring notes in the table below.

For addition information on review notes, please have a look at the first 500 Review Notes or the download full csv or tsv archives.

GloBI Review Badge

As part of the review, a review badge is generated. This review badge can be included in webpages to indicate the review status of the dataset under review.

Note that if the badge is green, no review notes were generated. If the badge is yellow, the review bots may need some help with interpreting the species interaction data.

GloBI Index Badge

If the dataset under review has been registered with GloBI, and has been succesfully indexed by GloBI, the GloBI Index Status Badge will turn green. This means that the dataset under review was indexed by GloBI and is available through GloBI services and derived data products.

If you’d like to keep track of reviews or index status of the dataset under review, please visit [GloBI’s dataset index ^[At time of writing (2024-11-18) the version of the GloBI dataset index was available at https://globalbioticinteractions.org/datasets for badge examples.

Discussion

This review aims to provide a perspective on the dataset to aid in understanding of species interaction claims discovered. However, it is important to note that this review does not assess the quality of the dataset. Instead, it serves as an indication of the open-ness⁵ and FAIRness (Wilkinson et al. 2016; Trekels et al. 2023) of the dataset: to perform this review, the data was likely openly available, Findable, Accessible, Interoperable and Reusable. The current Open-FAIR assessment is qualitative, and a more quantitative approach can be implemented with specified measurement units.

This report also showcases the reuse of machine-actionable (meta)data, something highly recommended by the FAIR Data Principles (Wilkinson et al. 2016). Making (meta)data machine-actionable enables more precise procesing by computers, enabling even naive review bots like Nomer and Elton to interpret the data effectively. This capability is crucial for not just automating the generation of reports, but also for facilitating seamless data exchanges, promoting interoperability.

Acknowledgements

We thank the many humans that created us and those who created and maintained the data, software and other intellectual resources that were used for producing this review. In addition, we are grateful for the natural resources providing the basis for these human and bot activities.

Author contributions

Nomer was responsible for name alignments. Elton carried out dataset extraction, and generated the review notes.

References