Speakers: Mars Benzle and Jane Wyngaard
https://datascience.nih.gov/CaseStudy
After a brief overview, GOAT attendees broke into groups, according to those 4 entities to discuss gains and barriers.
Open Data discussion in regard to farmers
"Open data” in this context is focused on three types of farm data:
Ways to acquire data
Opportunities
Barriers
Prompt: What are the barriers and gains of participation and investment in open source agriculture data?
Government perspective:
Gains:
• Save money on surveying and surveys.
• Tax payers can better see the befits of research being done.
• Tools to see and benefit from data.
• There could be better management of researchers or better communication between researchers. Researchers will have a better idea of what other researchers with similar goals are doing.
• More viable to have more democratic data collection.
• More possibility of community governance and participation. Some example is Data.gov.
• People saw the value of data collected in something like the NASA program.
• Like SBIR?
• Jobs?
• Market data can stabilize prices, create tighter supply chains, better supply chains, more evenly distribute food systems, AMIS for example, or the anonymized avocado price data project.
• Environmental data
• A more distributed system could be a gain but it could also be a problem.
• More Economic data for financing people who are better suited to be funded.
• People love free
• People don’t want to put in the work to collect their own data.
• More redundancy to understand wider trends.
Barriers
• People like farmers don’t trust the government which hinders what programs the government can implement.
• Taxpayers don’t want more money to go to the government.
• No unified chain of communication for how data will be gathered, managed, and distributed.
• There needs to be restrictions to protect individual privacy.
• Governance of how data is maintained and shared.
• Discomfort and problems with unchecked government power.
• Misuse of data who drives what is collected in the first place.
• Which data sets are aggregated and used.
• What outcomes are preferable?
• How do we insure all data is voluntarily submitted?
• Data can “vanish“ if political values change between admins.
• Does the government own the data it shares?
• Misrepresentation of data through stats.
• Disinterest by the government to advocate for open source.
• Are there regulatory barriers that already exist?
• Commercial spinoff?
• National security concerns of foreign governments tracking water or Natural resources.
• Foreign powers looking at our food system or food insecurities and using that against us.
• The commercial agriculture lobby.
• What is the economic value of OS data
• Management of a huge quantity of data (technical storage)
• Capacity- data management could put a stress on human and economic resources.
• The use of electricity and power in data storage and management.
• Patents could result in positive commercial development or negative monopolies if commercial entities use the os data to develop non competitive markets.
• Broadband is not up to snuff in many areas.
• How do you verify if the data is good? Could the system succumb to data trolls or data terrorists or any one that benefits to the manipulation of the data points?
• Is such technology accessible to people how do we get them access?
• Cultural acceptance of OS some governments want control for their own reasons.
• Free is cheap or not good.
• If it is free how to you have technical support in how to use it?
• How will we insure its persistence through time (soft money)
• Lack of a shared technical language, share units, shared natural language, vocabulary. How do people interface?
Other notes from the group:
There are two issues with government and open source data there is the governments’’ willingness to contribute and participate and the people’s willingness to allow the government to collect large amounts of data on individuals and share it.
Research perspective:
Opportunities:
• Collaboration around data
• Share data with farmers
• Creating communication.
• Creative commons but for data sharing
• Education and training on research data
• Not reinventing wheel to test machine learning
• Data citation opportunity
• Many farmers-data points
• Can better calibrate and validate models
Challenges:
• Data Quality (How good is it? Cal/Val). People want to make sure it is right before publishing
• Disincentives in academia because of wanting to publish papers
• Producer data-commodity
o Geocoding
o Privacy
• Data in form that farmers can use.
• Assumptions about data
o Researchers need to shar it in a meaningful way
• How do researchers get farmers to collect data?
• Competition for grants
• Documentation
o Metadata
o Education self documenting
Where:
• Git Hub
• Library at university
• USDA
• Repository
Questions:
How are technologies changing the knowledge process?
How do we built trust between stakeholders?
Other notes from the group:
Social vs environmental data: the same challenges and opportunities apply. You could leverage more connection and collaboration with shared interests.
Main points:
Commercial perspective:
Pros
Cons
Challenges