One of the tenets of big data is the idea of the (2,4, 7) V’s - Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. With the increase in the volume and velocity of data, access becomes ever more challenging. Users have access to more types of data and they can become overwhelmed by the possibilities. In the past, data access has been confusing but now there is more user engagement in building friendlier and more usable interfaces. Discovery is now more flexible and all encompassing - for example using schema.org to enable data discovery and via Google search. This increased use of data is not limited to scientists and other professionals. Citizens use data more than they realize (maps, elevation charts, tides, etc.) so they are constantly accessing data from a variety of sources.
There remains a broader community goal to have improved data access with the aim of democratizing data by removing gatekeepers so that data are unrestricted and available in a meaningful way to all. Improved access to data also supports data equity - “The term “data equity” captures a complex and multi-faceted set of ideas. It refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted, and distributed.” By making data more easily accessed and used we also make the ability to use data more equitable.
We want to gather a set of papers that bring together all aspects of the data access process with a focus on improving data access for a wide range of users. We propose the following structure:
· data discoverability
· data access
· data and service equity
· data usability
· user interface/engagement/input
· visualization tools
· reproducibility and tracing - after access:
Questions we aim to address include:
1. What is the future of data access when the supercomputer of yesterday is the device in our pocket today?
2. Access is important but what about reproducibility?
3. Access is important but what about understanding? The importance of quality metadata.
4. How can tools such as links to data Data Object Identifiers (DOIs) answer questions about data quality.
5. What kinds of data access tools are needed to support FAiR?
6. Are data access tools fundamentally different if you are working entirely in the cloud - from data receipt from a sensor, to QA/QC, use in a model and, finally, analysis?
7. Six strategies for data discovery and access?
8. How to provide access to data to answer the users question- ie, meaningful access (“People are not looking for data… They are looking for answers to their questions”)
?Themes for this issue include, but are not limited to:
· Data access and IOT devices such as AUVs
· Environmental services for both the atmosphere and the ocean
· Improvements to access via the use of cloud computing
· Advantages, and disadvantages of, crowdsourced data
· Improving access to environmental data to support economic and socio-economic analyses with in support of coastal planning
· How improved data access contributes to the Blue or Green Economies?
· Techniques for machine to machine support of data access
· Data discoverability/access for value added datasets (products) to meet users needs
· Data discoverability via mobile devices
· Directed data query ? purposeful query (i.e., Wedding Day Planner; Vacation Planner).
· User engagement - usability in expert vs. general portal development
· How improved data access enriches the lives of everyday citizens
· Visualization techniques of old are no longer viable in newer spatio-temporal datasets. What are some new creative approaches to data visualization in the future?
· Interactive visual dynamics - interacting with intelligent visualizations can form the basis of analysis.
One of the tenets of big data is the idea of the (2,4, 7) V’s - Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. With the increase in the volume and velocity of data, access becomes ever more challenging. Users have access to more types of data and they can become overwhelmed by the possibilities. In the past, data access has been confusing but now there is more user engagement in building friendlier and more usable interfaces. Discovery is now more flexible and all encompassing - for example using schema.org to enable data discovery and via Google search. This increased use of data is not limited to scientists and other professionals. Citizens use data more than they realize (maps, elevation charts, tides, etc.) so they are constantly accessing data from a variety of sources.
There remains a broader community goal to have improved data access with the aim of democratizing data by removing gatekeepers so that data are unrestricted and available in a meaningful way to all. Improved access to data also supports data equity - “The term “data equity” captures a complex and multi-faceted set of ideas. It refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted, and distributed.” By making data more easily accessed and used we also make the ability to use data more equitable.
We want to gather a set of papers that bring together all aspects of the data access process with a focus on improving data access for a wide range of users. We propose the following structure:
· data discoverability
· data access
· data and service equity
· data usability
· user interface/engagement/input
· visualization tools
· reproducibility and tracing - after access:
Questions we aim to address include:
1. What is the future of data access when the supercomputer of yesterday is the device in our pocket today?
2. Access is important but what about reproducibility?
3. Access is important but what about understanding? The importance of quality metadata.
4. How can tools such as links to data Data Object Identifiers (DOIs) answer questions about data quality.
5. What kinds of data access tools are needed to support FAiR?
6. Are data access tools fundamentally different if you are working entirely in the cloud - from data receipt from a sensor, to QA/QC, use in a model and, finally, analysis?
7. Six strategies for data discovery and access?
8. How to provide access to data to answer the users question- ie, meaningful access (“People are not looking for data… They are looking for answers to their questions”)
?Themes for this issue include, but are not limited to:
· Data access and IOT devices such as AUVs
· Environmental services for both the atmosphere and the ocean
· Improvements to access via the use of cloud computing
· Advantages, and disadvantages of, crowdsourced data
· Improving access to environmental data to support economic and socio-economic analyses with in support of coastal planning
· How improved data access contributes to the Blue or Green Economies?
· Techniques for machine to machine support of data access
· Data discoverability/access for value added datasets (products) to meet users needs
· Data discoverability via mobile devices
· Directed data query ? purposeful query (i.e., Wedding Day Planner; Vacation Planner).
· User engagement - usability in expert vs. general portal development
· How improved data access enriches the lives of everyday citizens
· Visualization techniques of old are no longer viable in newer spatio-temporal datasets. What are some new creative approaches to data visualization in the future?
· Interactive visual dynamics - interacting with intelligent visualizations can form the basis of analysis.
