Note: the published version of this document is also available at https://hackmd.io/s/Sk6Xa7Eq-#.
The Phenoscape project is hosting a hands-on workshop on Dec 11-14, 2017, at Duke University in Durham, North Carolina.
Evolutionary phenotype data that is amenable to computational data science, including computation-driven discovery, remains relatively new to science. Therefore use-cases and applications that effectively exploit these new capabilities are only beginning to emerge. If you are interested in discovering, linking to, recombining, or computing with machine-interpretable evolutionary phenotypes, this is the workshop for you!
The event will bring together a diverse group of people to collaboratively design and work hands-on on targets of their interest that take advantage and promote reuse of Phenoscape's online evolutionary data resources and services. The event is designed as a hands-on unconference-style workshop. Participants will break into subgroups to collaboratively tackle self-selected work targets.
To apply to participate in the event, please fill out the following form by Oct 9, 2017: https://goo.gl/tt18gR Travel sponsorship is available but limited, as is space. We expect to notify applicants about acceptance starting one week after application due date.
Updates will be posted to the workshop repository on Github: https://github.com/phenoscape/KB-DataFest-2017
We are looking to assemble a diverse group of people, including developers, computer scientists, data scientists, and domain experts from a variety of relevant fields, such as evolutionary science, ecology, biodiversity, biomedical sciences, and bioinformatics. Example personas we are looking for include, but aren’t limited to the following:
- Biology / biodiversity / ecology scientist
- Evolutionary medicine researcher
- Software / tool / resource developer
- Data semantics researcher
- Data product developer
- Training / documentation specialist
- Visual and data interaction designer
Everyone participating in the event must agree to adhere to its Code of Conduct.
We are keeping the scope into which work targets at the event are expected to fall intentionally broad, so as not to limit participants' ideas a priori. That said, we generally expect work targets to use data and/or the computational services of Phenoscape's online resources (see About the KB below) in some way, whether through available APIs or not.
Possible targets can take many forms. Examples include, but are not limited to the following:
- Connecting one's data, tool, or database to the KB (to address a research question, or to interconnect a community resource);
- Increasing visibility into the KB data, such as through visualization, mashups, or apps;
- Devising resources that make it easier to reuse the KB's data or capabilities, for example in the form of vignettes;
In general, work targets that align well with participants’ own professional interests are generally more desirable than others that do not, because they typically get more traction, and are more likely to be continued in some way after the event.
Efficiently repurposing, integrating, and mining the vast stores of phenotype data has long been hampered by the limited amount of data accessible online in standard formats, and by the challenges involved with enabling machines to compute with data that is largely recorded in natural language text descriptions. A variety of advances has begun to address these challenges, including powerful knowledge representation technologies, shared domain ontologies, very fast machine reasoners, and various tools accelerating the construction of large databases of ontology-linked phenotype data. The results emerging from these advances provide new opportunities for computation-driven data science with phenotype data, and specifically for natural biodiversity a unique resource for data science-enabled phenotype descriptions is the Phenoscape KB.
The Phenoscape Knowledgebase (KB; http://kb.phenoscape.org) is a resource containing evolutionary phenotype data from more than 150 comparative morphology studies, with a focus on the vertebrate fin-to-limb transition and comparative fish morphology. The KB offers programmable (API) access to natural language phenotype descriptions annotated with formal ontologies so that machines can understand and compute with the semantics of descriptions at scale. Using shared ontologies for morphological, spatial, and other requisite domain knowledge, the KB links these data to phenotypes reported in genetic perturbation studies for pertinent model organisms (zebrafish and mouse), and to human genetic disease phenotypes.
For query answering the KB uses machine reasoning to match data by its semantics. The API also gives access to other machine reasoning-based algorithms. These include synthesizing characters and states that are implied by, but not expressly asserted, in the original studies; and finding evolutionary phenotype transitions semantically similar to gene phenotypes.
Further details, including relevant publications, KB data model, and API documentation can be found at the workshop wiki: https://github.com/phenoscape/KB-DataFest-2017/wiki/Learning-about-the-KB
The overall objective of the workshop is to foster broader adoption, reuse, and interoperability of computable phenotype data in general, and of the data in the Phenoscape KB, and its machine reasoning capabilities, in particular.
More specifically, the event aims to enable more tools, resources, and researchers to better integrate with and take advantage of the data and computational services provided by the KB. As a desired side-effect, this will ultimately help the Phenoscape team to establish concrete use cases driven by community needs, inform requirements, and prioritize targets for future development of the KB, its API, and its services.
- Scott Chamberlain (rOpenSci)
- Matthew Collins (University of Florida, iDigBio Informatics)
- Melissa Haendel (Oregon Science & Health University, Monarch Initiative PI)
- Hilmar Lapp (Duke University, Phenoscape co-PI)
- Emily Jane McTavish (UC Merced, Open Tree of Life)