Network Ready Research:
The Role of Open Source and Open Thinking
OVERVIEW: The highest principle of network architecture design is interoperability. Metcalfe's Law says a network's value can scale as some exponent of the number of connections. Our job in building networks is to ensure that those connections are as numerous, operational, and
READINGS:
Molloy, J. C. (2011). The open knowledge foundation: open data means better science. PLoS biology, 9(12), e1001195.Whyte, A., & Pryor, G. (2011). Open science in practice: Researcher perspectives and participation. International Journal of Digital Curation, 6(1), 199-213.
"How do you support the unexpected?" This is the best single statement/question raised at the summer school till now. I think this touches the point of the evolution of intelligence and catalyzing innovation. Normally any dogma is always busy at reinforcing itself and does very little to stay open to those opportunities of profound change that often present themselves unexpectedly.
ReplyDeleteOne way to identify opportunities for change is by examining the dynamics of systems. Peter Csermely (http://www.amazon.com/Weak-Links-Universal-Stability-Collection/dp/3540311513) developed a few criteria that identify turning points in network dynamics. Might be a direction to address the problem.
ReplyDeleteI found quite interesting Cameron Neylon’s discussion of different kinds of openness. One of the main points I took home from this talk is that being open, over and above questions of open access to data, means that “my work can help someone.” How are the different kinds of openness related? Can they “catalyze” each other?
ReplyDeleteTalking about sharing, how do you get people to recognize your contribution when your facing, say, credibility prejudice? (in Miranda Fricker's sense)
ReplyDeleteTherefore we expect to be rewarded? I think we do not need to be rewarded. It's true we need money for almost everything. No plans for unexpected. However we focus in the present. We are following the technologies innovations. We are already in the transition.
ReplyDeleteThe idea to exploit an existing network’s resources in order to make an innovative project it seems very interesting idea, however the difficulty here is about how to estimate the cost of this kind of project (comparing doing the project with expert people vs with large public). For me, researchers can’t go with a project without having the right estimate for the next step.
ReplyDeleteJ’ai trouvé la question de savoir si une collaboration massive en mathématique est réellement productive ou le contraire. Il me semble qu’il est important de relativiser la productivité de la collaboration en fonction du domaine d’étude.
ReplyDeleteTranslation:
DeleteCollaboration varies with the field.
Dr. Neylon's talk went very nicely with Dr. Gloor's ideas of intrinsically motivated innovation and Dr. Heylighen's ideas about alignment and the reduction of friction. I will ponder Dr. Neylon's questions about how to support the unexpected. I like that he stresses usability and reach in whatever research people will choose to pursue.
ReplyDeleteI find the metaphor of reducing friction very powerful. It's all about building infrastructure to support an activity. How can we apply this to open source research? Where is the friction which prevents open source research from taking off, and how can it be reduced?
ReplyDeleteI think you means either open access (to research article) or open data, Nicole. "Open source" is specific to software (open your code, or even give it away). The friction with articles is not the authors but the publishers. With data it is the author's need of first-exploration rights and with software it's whether or not they want to reveal it, and/or to sell it...
DeleteWhat you're saying here relates well to what has been discussed about data visualization and different semantic networking strategies. When it is difficult to understand data that is presented in an unintuitive way, this creates friction for the user. Similarly, when different companies use proprietary strategies to create semantic webs, leading to an incompatibility between systems, we, as a user community, cannot benefit from the combined efforts of multiple groups. Jim Hendler touches on this in his talk.
DeleteDuring the discussion after these presentations we talked about how open we can, or should, be with our data. Dr. Harnad pointed out that he wouldn’t mind publishing his data as soon as he acquired it (very commendable), but that some of his friends would. Although I would like to share Stevan’s opinion, I think I fall into the same category as his friends.
ReplyDeleteWhen working on research, or any other project, there is always one step that appears way bigger than all other steps. It feels somewhat disappointing for someone to collect all the data just to have someone else take their hard work and complete the final step. Both our school system and industry encourage my camp. We are evaluated individually. If we don’t highlight our contribution we get little recognition. I’m sure we can manage to share data immediately, we’ll just need to consider some things ahead of time.
Dear Cameron, Thank you to share your ambitious visions ! I have one question : I’m working on the way of a selection of Wikipedia articles can improve the understanding of a specific scientific article and I’m studying which models are better to predict the dynamic construction of knowledge. I would like to work with ontologies. Do you think it’s possible to model the possible positive rythm of knowledge diffusion thanks to open access? Which ontological, statistical or machine learning models can we use for that?
ReplyDelete