Title: Semantically Enhanced Model Experiment Evaluation Process SeMEEP within the Atmospheric Chemistry Co
1Semantically Enhanced Model Experiment Evaluation
Process (SeMEEP)within the Atmospheric Chemistry
Community
-
- Chris Martin 1,2, Mo Haji 2, Peter Dew 2, Peter
Jimack 2, Mike Pilling 1 - 1 School of Chemistry, University of Leeds
- 2 School of Computing, University of Leeds
2Outline of the Presentation
- Introduction
- Atmospheric community
- SeMEEP
- ELN Provenance capture
- Conclusion and next stage
3Section 1 Overview
- Application domain atmospheric community
- Reliance on computational models to evaluate
data - Motivation
- Study how to transition from today's ad-hoc
processes practises - Sustainable process of
- Gathering, community evaluation and sharing data
models between scientists - Minimising changes to proven working practises
of the scientist - Within world-wide co-laboratories
4Related projects
- CombeChem
- Experimental organic chemistry
- From source to long term data
- perseveration (knowledge)
- Semantically-enabled ELN
- Data-driven workflow
- Collaboratory for Multi-Scale Chemical Science
- Multi-layer chemical model
- myGrid
- Bio-informatics and related areas (semantic
pattern matching - Reusable semantic workflow using SMD (semantic
metadata) - Data Quality
- Karama2
- Weather forecasting computation modelling
5Section 2 Atmospheric Chemistry
- Seeks to understand the chemical processes
(reactions) taking place in the lower atmosphere
(e.g. smoke) - It has significant implication for both
- Air Quality
- Climate Change
6The Master Chemical Mechanism (MCM)
- Data repository of elementary chemical reactions
rate constants - The mechanism is described by a computational
model that is evaluated against experimental data - Chamber experiments
- Field experiments
7Section 3 SeMEEP
- Today
- Typically within the atmospheric chemistry
community the provenance is recorded in an
ad-hoc, unstructured fashion, using a combination
of traditional lab-book, word processing
documents and spreadsheet. - Move to more sustainable evaluation process
supports the gathering, evaluation and sharing of
data and models - Using semantic metadata
8SeMEEP Vision
- SeMEEP semantically-enabled MEEP
- Supports the organisation of information but
critically, records its provenance (say to
recover secondary data)
Mike Pilling SeMEEP approach will radically
enhance the effectiveness of a research community
to deliver new science
9Requirements with metadata for elementary
chemical reactions
10Requirements for metadata capture for elementary
reactions
- Only published data
- Rate constants from several labs
- No access to the raw data
- No access to secondary data
- SeMEEP will provide this.
11Current Evaluation Processes for the MCM
12Envisioned Evaluation Processes
13Section 4 Electronic Lab-Books (ELNs)
- ELNs address the limitations of the current
methods of provenance capture. - Southampton ELN for organic chemistry
experiments. - Benefits to the modeller
- Modelling process can be automatically captured
- Searchable
- Remote access is possible
- Provenance is structured
- Possible to use resolvable references to
resources
14Will User attach quality metadata?
- Motivate users
- By demonstrating the value of provenance in their
day-to-day work - Writing publication
- Managing their data
- Reinterpretting the data.
- Management
- Publishers
15Recoding provenance for modelling
- 3-level process
- Experimental modelling plan
- Modelling iterations
- Modelling layer where provenance is capture has
the modelling process proceeds using data-driven
workflows where data is a first class object
16The Modelling Process - A Three Layer Mapping
17MCM Mechanism being investigated
18ELN Process
19ELN Screenshots
- Prompts displayed when changing the changing the
chemical mechanism - Editing a reaction
- Adding a new reaction
20ELN Screenshots
21ELN Modelling SMD Architecture
22Evaluation Methodology
- In-depth interviews with members of the
atmospheric chemistry model group here at Leeds,
covering - Demonstration of the prototype
- User testing of the prototype
- Discussion of scenarios involving the use of the
prototype (e.g. ) - Analysis
- Interviews recorded and transcribed
- Analysed using techniques from grounded theory
23Evaluation
- Barriers to adoption
- Effort required at modelling time for provenance
capture - in your lab book you can write down what ever
you want but with an ELN it is going to take
time to go through the different protocol steps.
- When asked if they would use an ELN requiring a
similar amount of user input to the prototype the
response was positive - Yeah, I think it would be a good thing. I dont
think it is too much extra work. - Rather than viewing the prompts for user
annotation as interruption to their normal work
the user recognised the value of being prompted - is a good way to do it because otherwise you
wont record the provenance.
24Evaluation
- Users intuitively grasped the benefits of
recording provenance with an ELN and that the
benefits would be realised after the time of
modelling by a number of stakeholders - if someone else wants to look at your
provenance, thats great because the person can
see exactly what you have done, where you have
been and where to go next. And for yourself, if
you are writing up a PhD ... you can see
exactly what youve done whereas currently you
have to rifle through lab-books to see exactly
what you have done.
25Section 5 Conclusions and future work
- Outlined SeMEEP and ELN
- User evaluated proposed modelling ELN
- Addressed case studies
- IUPAC
- MCM
- Developing a case study with the Geomagnetic
community - User and System issues
- Application of actively theory to capture
requirements and user evaluation - Querying and inference
- Address QoS issues (e.g. security, scalabilty,
dynamic roles-based access control)
26Questions