11 Matching Annotations
  1. May 2022
    1. The paper describes four ontologies for representing workflows in Research Objects, and includes examples and motivation scenarios.

      The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate how the ontologies can be utilized using a real-world scenario, in which scientists created a Workflow Research Object for an investigation on the Huntington's disease. We also present the tools we developed for managing Workflow Research Objects.

      A sketch depicting the main steps that the bioinformatician followed for manipulating and analyzing datasets, and the workflows that were used in each step

  2. Apr 2022
    1. Since most of our feeds rely on either machine algorithms or human curation, there is very little control over what we actually want to see.

      While algorithmic feeds and "artificial intelligences" might control large swaths of what we see in our passive acquisition modes, we can and certainly should spend more of our time in active search modes which don't employ these tools or methods.

      How might we better blend our passive and active modes of search and discovery while still having and maintaining the value of serendipity in our workflows?

      Consider the loss of library stacks in our research workflows? We've lost some of the serendipity of seeing the book titles on the shelf that are adjacent to the one we're looking for. What about the books just above and below it? How do we replicate that sort of serendipity into our digital world?

      How do we help prevent the shiny object syndrome? How can stay on task rather than move onto the next pretty thing or topic presented to us by an algorithmic feed so that we can accomplish the task we set out to do? Certainly bookmarking a thing or a topic for later follow up can be useful so we don't go too far afield, but what other methods might we use? How can we optimize our random walks through life and a sea of information to tie disparate parts of everything together? Do we need to only rely on doing it as a broader species? Can smaller subgroups accomplish this if carefully planned or is exploring the problem space only possible at mass scale? And even then we may be under shooting the goal by an order of magnitude (or ten)?

  3. Feb 2018