Computers, the internet and mobile phones have changed how archaeologists work. More importantly it has changed how everybody can access, use and contribute to archaeology.
This has altered public expectations on modes of engagement and resource access. This is resulting in an increased demand for access to this data. This phenomena is not solely about archaeology and heritage but is reflected in many areas of society. Some governments have recognised that taxpayers, as funders of data, should be allowed to access and utilise this data more easily. This has underpinned the Open Data movement.
At the same time companies and institutions, like Google and NASA, started making large datasets available on the internet. Some of these organisations provided Application Programming Interface (API's) and other services so that software applications could be built around their data. Such software services made it easier for people to use this data to make new things (derive content) and in turn share these things with their communities. This produced the crowd-sourcing and citizen-science movements. Crowdsoucing is where products, ideas, or content are created by soliciting contributions from a large group of people online. The community mapping system called Open Street Map is a good example of crowdsourcing.
Other people want to be more active. Projects like Galaxy Zoo, Ancient Lives and Old Weather have helped free data trapped in books or help scientists collect and analyse data. National Geographic have sponsored a project to help detect archaeological sites in Mongolia using high spatial resolution satellite images (exploration.nationalgeographic.com/mongolia/home). With lots of people working together a big problem can turn into a small problem. These people are 'citizen scientists'.
This presentation will describe these movements in more detail and provide examples of their implications for the heritage sector. A vision will then be set out for the future of a collaborative framework for heritage management. This will be framed in the implications it has for practice, engagement, research, curation and policy. Public participation is welcomed!
2. Housekeeping
• Presentation is available on slideshare:
–http://goo.gl/Ew2Iz
●
My notes from the conference:
–http://goo.gl/dzOuz
●
Don't worry it's on the final slide
19. The past is a foreign place
• Archaeological
knowledge
acquisition is a
dynamic process
• Dynamic
feedback allows
theories/practice
to be tested or
revised
Interpretation
Synthesis
20. • Primary data
– Excavation
records
– Remote
sensing
transcriptions
– NMP
– Lab Analysis
– Specialist
reports
• Decoupled
synthetic data
– Site reports
– SMR
– NMR
24. Implications of silo-ed data
• No synergy
• Cripples the
knowledge
frameworks
• Less effective
– Research
– Impact
– Policy
– Engagement
Interpretation
Synthesis
33. Make better decision based on the best available evidence.Make better decision based on the best available evidence.
>K2 <U2
Known knowns
Known unknowns
Unknown
unknowns
72. Think provision not possession
Leif Isaksen
The next decade belongs to distributed
models not centralised ones, to
collaboration not control, and to small
data not big data.
Rufus Pollock
We must find new ways of doing our work in a
public sphere without impacting on
disciplinary integrity
Conor Newman
73. Think provision not possession
Leif Isaksen
The next decade belongs to distributed models not
centralised ones, to collaboration not control, and to
small data not big data.
Rufus Pollock
We must find new ways of doing our work in a public
sphere without impacting on disciplinary integrity
Conor Newman
Presentation is available on slideshare:
http://goo.gl/Ew2Iz
My notes:
http://goo.gl/dzOuz
Much of the material is freely available under CC-BY licences on Wikimedia
Commons: http://commons.wikimedia.org/wiki/Special:ListFiles/Arbeck
Notas del editor
Understand complex relationships in the fragmented archaeological record Archaeology as human ecology
Data Rich Data Archiving - Building the silo
Formal structures inhibit collaboration and access Informal networks established to make the data work effectively
Additional drops of data/evidence does not affect the structure of the knowledge landscape
Jorge and Ralfs software is available under a GPL 'copyleft' licence Side issue: Satellites can achieve much better than 50cm ground resolution – they are limited by american statutes to 50cm
The nature of knowledge From a policy perspective there are different levels of knowledge awareness know what we know (the data we have access to) know there are things we don't know (the relevant data which is not accessible) and recognise there are things that we are unaware of which may be extremely important (the potential knowledge advances gained by integrating all data, collaborating with different domains and future research avenues). Ideally we want to increase the size of the accessible knowledge so that policy can be formed from a position of ‘perfect’, or ‘near-perfect’, knowledge.
We can do more than this. Consider using the community to add value to all of the 'potential' sites that Ralf and Jorge are picking up at Baden Wuttenburg
Mention that the background maths engine is important. Assumptions in each engine mean that different environments produce different results
Showing your working Peer review of that working (think github)
Formal structures inhibit collaboration and access Informal networks established to make the data work effectively
