1 Software development WP6. 2 Team CEFAS: architecture and documentation IC: modules for calculating new functions (penalty prob.; enforcement cost) AZTI:

Slides:



Advertisements
Similar presentations
Chapter 5 One- and Two-Sample Estimation Problems.
Advertisements

Requirements Engineering Processes – 2
Software Requirements
George Spyrou, Financial Manager Navigo Shipmanagers, Limassol, Cyprus
kareRCIserIsviFIsaRsþGnuvtþKMerag
Slide 1 Insert your own content. Slide 2 Insert your own content.
Chapter 24 Quality Management.
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 3.1 Chapter 3.
ASYCUDA Overview … a summary of the objectives of ASYCUDA implementation projects and features of the software for the Customs computer system.
UNCTAD BrusselsIRU / UNCTAD June 2001Page 1 ASYCUDA++ SAFETIR Interface 1.Objective & Constraints 2.ASYCUDA++ Transit Module(s) 3.SAFETIR Implementation.
Towards a simpler and more efficient BR June 19, 2007 ICES-III Montréal (QC)
Quality Improvement in the ONS Cynthia Z F Clark Frank Nolan Office for National Statistics United Kingdom.
By Rick Clements Software Testing 101 By Rick Clements
COBECOS SIXTH FRAMEWORK PROGRAMME PRIORITY 8.1 WP 4 – DATA HARMONISATION London, 5 – 7 September.
COBECOS mid-term meeting, 2-3 September 2008, San Sebastian Italian case study: GSA 9 bottom trawling fishery Status of model estimation at September 2008.
Brussels the 3rd of December COBECOS 1 COMPUTER MODEL Costs and Benefits of Control Strategies SIXTH FRAMEWORK PROGRAMME Policy-Oriented Research.
Brest, 27 – 29 of February COBECOS 1 COBECOS SIXTH FRAMEWORK PROGRAMME PRIORITY 8.1 Policy-Oriented Research Second progress meeting Brest, 27 th.
COBECOS Brest of February COBECOS SIXTH FRAMEWORK PROGRAMME PRIORITY 8.1 WP 4 – DATA HARMONISATION Brest of February 2008.
WP 6 progress Cefas, Imperial (IC), AZTI, JRC (all participants) San Seb – Sep 2008.
Improving imputation methodology in the Hungarian Central Statistical Office (HCSO) NTTS 2009 seminar, Bruxelles February 2009 Improving imputation.
2nd Year: Estimation of Theoretical Relationships WP 5.
2/22/2014 E U R O P E A N F O R E S T I N S T I T U T E The general objectives of NEFIS are maximising the value of existing data and databases by: coordinating.
0 - 0.
Addition Facts
The ANSI/SPARC Architecture of a Database Environment
1 9 Moving to Design Lecture Analysis Objectives to Design Objectives Figure 9-2.
Making the System Operational
WS-JDML: A Web Service Interface for Job Submission and Monitoring Stephen M C Gough William Lee London e-Science Centre Department of Computing, Imperial.
STATISTICAL INFERENCE ABOUT MEANS AND PROPORTIONS WITH TWO POPULATIONS
1 Implementing Internet Web Sites in Counseling and Career Development James P. Sampson, Jr. Florida State University Copyright 2003 by James P. Sampson,
GUEST SERVICES GUIDE (c) Marin Management, Inc Revenue Management Guest Services Management Guide A. The Purpose of This Policy The management.
Integrating Academic Skills Development in a First Semester Chemistry Unit Technology Enhanced Curriculum | Sydney Teaching Colloquium | 2012 ASSOCIATE.
Insert image here © SPEC-Soft SAVINGS AND EXPERTISE FOR YOUR PLANT PFS-Suite Life-cycle Tools For Process Automation PFS-Suite TM.
Configuration management
Software change management
1 Dr. Ashraf El-Farghly SECC. 2 Level 3 focus on the organization - Best practices are gathered across the organization. - Processes are tailored depending.
Session # 2 SWE 211 – Introduction to Software Engineering Lect. Amanullah Quadri 2. Fact Finding & Techniques.
Kelly Weyrauch
Test plans. Test Plans A test plan states: What the items to be tested are At what level they will be tested What sequence they are to be tested in How.
5.9 + = 10 a)3.6 b)4.1 c)5.3 Question 1: Good Answer!! Well Done!! = 10 Question 1:
CHAPTER 2 – DISCRETE DISTRIBUTIONS HÜSEYIN GÜLER MATHEMATICAL STATISTICS Discrete Distributions 1.
Solving Absolute Value Equations Solving Absolute Value Equations
Enhancing Spotfire with the Power of R
Addition 1’s to 20.
Test B, 100 Subtraction Facts
Week 1.
10-1 © Prentice Hall, 2004 Chapter 10: Selecting the Best Alternative Design Strategy Plus Project Management Concepts.
©2008 Prentice Hall Business Publishing, Auditing 12/e, Arens/Beasley/Elder The Impact of Information Technology on the Audit Process Chapter 12.
12. NLTS2 Documentation: Quick References. 1 Prerequisites Recommended modules to complete before viewing this module  1. Introduction to the NLTS2 Training.
Bottoms Up Factoring. Start with the X-box 3-9 Product Sum
Chapter 11: Systems Development and Procurement Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Chapter
© Prentice Hall CHAPTER 11 Facilitating User Computing.
Harmonisation of Data. Objectives Develop a standard data description applicable to all fisheries Provide data necessary for the estimation of theoretical.
1 FLR: An Open-Source Framework for the Evaluation and Development of Management Strategies L.T. Kell, I. Mosqueira, P. Grosjean, J-M. Fromentin, D. Garcia,
Brief Overview of Data Processing of Afghanistan Household Listing, Pilot Census Results, Population and Housing Census and NRVA Survey Brief Overview.
Background Data validation, a critical issue for the E.S.S.
INFORMATION SYSTEM APPLICATIONS System Development Life Cycle.
Introduction to SDLC: System Development Life Cycle Dr. Dania Bilal IS 582 Spring 2009.
1 Software development WP6. 2 Application of FLR Generic Functions from Theory Key parameters (theory): q,x,shadow value,enf. cost, prob fine,f Case study.
San Sebastian 2 – 3 rd of September COBECOS 1 COBECOS SIXTH FRAMEWORK PROGRAMME PRIORITY 8.1 Policy-Oriented Research Mid-term meeting San Sebastian,
Software Evaluation Criteria Automated Assignment Applications RSCoyner 10/8/04.
1 FLR: An Open-Source Framework for the Evaluation and Development of Management Strategies L.T. Kell, I. Mosqueira, P. Grosjean, J-M. Fromentin, D. Garcia,
Examples of Computing Uses for Statisticians Data management : data entry, data extraction, data cleaning, data storage, data manipulation, data distribution.
Advanced Higher Computing Science The Project. Introduction Worth 60% of the total marks for the course Must include: An appropriate interface using input.
EU-FP6-EFIMAS Participatory Fisheries Management Evaluation Frameworks
System Design, Implementation and Review
Generic Statistical Business Process Model (GSBPM)
Do local social problems need centralized statistics?
Agenda Context of the BR Redesign Redesign Objectives Redesign changes
Presentation transcript:

1 Software development WP6

2 Team CEFAS: architecture and documentation IC: modules for calculating new functions (penalty prob.; enforcement cost) AZTI: links FLR to databases JRC: testing solutions for less experienced users (web access version)

3 WP 6 software Description of work

4

5 Planning Discussed at meeting (14 June – London) WP 6 Software WP 3 Theoretical Modelling WP 2/4/5 Data collection Harmonisation Estimation WP 7 (IC) Model WP 8 Simulations

6 Feedback WP 6 Software WP 7 (IC) Model WP 8 Simulations r – the software used Also in r Or a module of FLR? MSE

7

8

9 Requirement for development of generic code Implement COBECOS theoretical approach on a case-study basis Each case-study has individual requirements Guidance can be provided Use bio-economic model versus Management Strategy Evaluation (MSE)– depends on resources, data, linkages to other projects. COBECOS requirements

10 Operating Model Computer simulation of complex "Reality" Assessment Computer simulation of sampling and assessment process Management Controls Computer simulation of management based on perception of the system Process errors are generated in the operating model. Data for assessments are generated with measurement error Estimation errors arise due to the assessment procedures. Assessment results in a Perceived system. Performance Statistics Feedback Performance Statistics Management Strategy Evaluation

11

12

13 Pros/cons Multi-fleet (<3 stocks) Parameterisation Integrating COBECOS code Provides shadow value of biomass

14 Case studies which can/will undertake MSE currently as part of another project – EFIMAS Northern hake, CCAMLR, Ligurian, (Dutch), Channel (if stock)

15 Code for computation of ? –Derive Other functions? (regressions etc) Within case study: resources required! (dedication of participants – at case study level) Work-plan