This document proposes a new approach to conducting scientific research that continuously updates hypotheses as new data becomes available over time. It involves ingesting relevant datasets, representing hypotheses as executable programs, and periodically re-running the programs on the aggregated data to test if conclusions have changed. As a case study, the document applies this approach to re-evaluate a published hypothesis about long-term climate trends in Australia over a wider timescale and geographical scope using continuously updated temperature data sources. It concludes the proposed approach leverages existing tools to support science through large-scale computation and data integration but challenges remain in fully representing the scientific inquiry process and its computational demands.

1. Jaakko Lappalainen
Computer Science department
University of Alcalá, Spain

2. Overview
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The problem
Proposed approach
The method
Results
Conclusions
Strengths and weaknesses
Future work
Questions

3. The problem
• Researchers focus on a particular time
frame and scope for testing their
hypotheses.
• But the conclusions of the research are
projected to the future.
• Paradox: the work that predicts things for
tomorrow, becomes a snapshot of what
happened until today.

4. Proposed approach
• New data relevant to some hypotheses
gets continuously aggregated as time
passes.
• With common semantics, it can be
combined or related to other datasets.
• Represent the hypothesis as programs
that are executed repeatedly.

5. The method
• The case of study
– Lenten, L. J., & Moosa, I. A. (2003). An
empirical investigation into long-term climate
change in Australia. Environmental Modelling
& Software, 18(1), 59-70.
• The authors claim that the temperature
series has some a trend feature.

6. The method (II)
• Let’s find some data sources.
– ACORN-SAT, from the Australian Bureau of
Meteorology. This uses LD!!
– NOAA weather data, not in LD but easy to
parse…
• Periodically ingest data (e.g., into a
relational database)
• An R script checks if the trend on the data
has changed…
• Ingested data is semantically tagged…

7. Results
• We are checking for Lenten & Moosa’s
hypothesis every week.
– More extensive time scope.
– Wider geographical scope, to all data
available for Australia.
• The snapshot becomes a movie.
• Executable paper

8. Conclusions
• The tools we already have allows us to
use large-scale computation
infrastructures easily to support science.
– The agINFRA project
• Massive data ingestion.
• Data integration and interlinking.
• User-tailored service execution.

9. Strengths
• Data availability
– The data is ingested (from LD sources, but
not only) and published.
• Data interoperability
– The data is not stored by itself.
• Actionable data
– Ready to be addressed, used and generate
new actionable data.

10. Weaknesses
• Represent ‘science inquiry’ as a data
model is not trivial.
• CPU-consuming tasks are even more
consuming.

11. Future work
• Further dataset interlinking
– More plural value for physical parameters.
– Dataset value error detection.
• Advance in hypothesis representation
– Machine readable research processes.

12. Questions?

13. Thank you very much!
jkk.lapp@uah.es