Supported by the EU-funded Biodiversity Group Built-in Data Library (BiCIKL) challenge, the gathering at Biodiversity Information Journal will present APC waivers for as much as 100 publications
A brand new article assortment, devoted to linked FAIR biodiversity knowledge was introduced by the EU-funded Biodiversity Group Built-in Data Library (BiCIKL) challenge.
The BiCIKL challenge is devoted to constructing new communities of key analysis infrastructures, researchers, citizen scientists and different stakeholders through the use of linked and FAIR biodiversity knowledge in any respect phases of the analysis lifecycle, from specimens by means of sequencing and identification of taxa, to remaining publication in superior, human- and machine-readable, reusable scholarly articles.
Supported by BiCIKL, the upcoming assortment at BDJ will present an thrilling alternative for biodiversity researchers to take pleasure in free and technologically superior publication for as much as 100 scholarly articles.
The gathering will welcome analysis articles, knowledge papers, software program descriptions, and methodological/theoretical papers that show the benefits and novel approaches in accessing and (re-)utilizing linked biodiversity knowledge.
The journal continues to be on the lookout for visitor editors to affix the core group. In case you are , please tell us at email@example.com.
On this assortment, the authors might want to be certain that their narratives adjust to the community-agreed requirements for phrases, ontologies and vocabularies. Moreover, they are going to be required to make use of express persistent identifiers, the place such can be found.
Listed below are a number of examples of analysis questions regarding semantically enriched biodiversity knowledge:
- How linking taxa or OTUs to exterior knowledge in my examine will contribute to a greater understanding of the capabilities and regional/native processes inside faunas/floras/mycotas or biotic communities?
- How mine and different researchers’ knowledge and narratives (e.g. specimen information, sequences, traits, biotic interactions and so on.) will be re-used to help extra in depth and data-rich research?
- Easy methods to streamline taxon descriptions and inventories, together with such primarily based on genomic and barcoding knowledge?
- How basic conclusions, assertions and citations in my article will be expressed in a proper, machine-actionable language?
- Different taxon- or topic-specific analysis questions that might profit from richer, semantically enhanced FAIR knowledge.
Circumstances for publication and kinds of articles:
- Manuscripts should use knowledge from at the least two of the BiCIKL’s partnering analysis infrastructures. Extremely welcome are additionally submissions that embrace knowledge from analysis infrastructures that aren’t a part of BiCIKL.
- Taxonomic papers (e.g. descriptions of recent species) should comprise persistent identifiers for the holotype, paratypes and nearly all of the specimens used within the examine.
- New species descriptions utilizing knowledge related to a specific Barcode Identification Quantity (BIN) imported instantly from BOLD through the ARPHA Writing Software are inspired.
- Particular person specimen information imported instantly from BOLD, GBIF or iDigBio into the manuscript are strongly inspired.
- Hyperlinked in-text citations of taxon remedies from Plazi’s TreatmentBank are extremely welcome.
- Different phrases of worth hyperlinked to exterior assets are inspired.
- Tables that record gene accession numbers, specimens and taxon names, ought to conform to the Biodiversity Information Journal’s pointers.
- Theoretical or methodological papers on linking of FAIR biodiversity knowledge are eligible for the BiCIKL assortment if they supply examples and use instances.
- Information papers or software program descriptions are eligible in the event that they use knowledge from the BiCIKL’s partnering analysis infrastructures, or describe instruments and companies that facilitate entry to and linking between FAIR biodiversity knowledge.