1. Community structures - a CrossRef for ALM?
What would be the optimal structure of community, to support coordination and collaboration. Do we need it? If so, when? And how would we tell?
Reasons to cooperate:
- Share costs
- Coordination with other (standards) groups
2. Advocacy and promotion:
There is a lack of inspiration and awareness outside this workshop.
- ALM advocacy is similar to OA advocacy effort
- Need new name to bring people together
Anti-gaming mechanisms, process interoperability:
- What is the difference between gaming and legitimate attention?
- How do we tell?
- How do we tell the people that care? Messaging is very important – technical explanations will not work.
4. Coverage, what to measure, what matters, to who?
What is currently scalably measurable and immeasurable and how do we make the immeasurable measurable?
- Create tools for publishing and citing
- Tools to crowdsource what can't be machine measured
- Focus on implicit data (footprints)
5. Semantic Analysis:
Current metrics lack context, such as sentiment, source, intensity etc. Context is multi-dimensional but users of metrics need low-metrics (eg. good/bad, for method).
- Need a good context vocabulary
- Need to map hurdles.
- Necessity for altmetrics as opposed to traditional citations.
6. Evidence, context, trust, and interpretation:
- How do you contextualise alms?
- What contexts matter to who?
- Do we get different information, or do they just all tell us the same thing, and is the current lack of context an opportunity to create our own narrative, or do we need to get with the program ASAP and on next to reality now?
The shorter versions are: What does "14" mean? & Where's my black line?
7. Personalization, use case targeting:
Altmetrics should enable scholars and institutions to present themselves to funders, peers and the public. Within this problem we considered it was relevant and important to:
- Track the identity of who generates the metric (i.e. who tweets)
- Reconsider the name of these activities to capture the idea of context-
- Consider other research outputs (beyond articles)
8. Data Interoperability:
In an ever more distributed and chaotic landscape our challenge is the reconciliation of that which is being counted and the counts themselves.
- How do we identify and reconcile copies of that which is being counted?
- There is a need to create and foster standards and best practice which allow understanding, comparison, and aggregation across source data providers and altmetric providers.
- We need to provide further context for the counts through normalization and identification of sources.
9. Too many measures, how to present? Single Number?
It is human nature to try to collapse complex data to a single # and given that oversimplification to one # is potentially dangerous…
How do we:
- Have transparency of measures and indicators
- Have at least as many measures as there are interesting things to measure
- Have an articulation of what the numbers represent