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Adding value:  Research data management at WorldFish Kirsten Abernethy & Eddie Allison
We have a data problem! The data we DON’T have: ●  Large scale, wide    geographic range ●  Baselines, panel data ●  Pover...
What does this mean for understanding fisheries? ●  No synthesized data on basic metrics of poverty and wellbeing, hinders...
FishMicroEcon <ul><li>●  Micro Economic Database of African Inland fisheries, HH and village surveys </li></ul><ul><li>●  ...
FishMicroEcon  - Synthesis of micro-econ/livelihood case studies <ul><li>Variables identified: </li></ul><ul><li>- Demogra...
FishMicroEcon: The questions Who is poor ? (HH level analysis, with some intra-HH data) What are the assets that define po...
Research Data Management Project (RDMP) <ul><ul><li>People: </li></ul></ul><ul><ul><li>Dr. Nicolas Bailly, Project Manager...
Research Data Management Project (RDMP) <ul><li>The specific objectives of the project: </li></ul><ul><ul><li>Phase 1. the...
Research Data Management Project (RDMP) We need help from the WFC scientists to complete phase 1     Your Input   Explanat...
Research Data Management Project (RDMP) Why you want to be involved! 1. Opportunity to do analysis at a larger scale = Exc...
Asanteni Natotela Twalumba Weebale Zikomo
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Science Forum Day 4 - Eddie Allison - Research data management at WorldFish

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Transcript of "Science Forum Day 4 - Eddie Allison - Research data management at WorldFish"

  1. 1. Adding value: Research data management at WorldFish Kirsten Abernethy & Eddie Allison
  2. 2. We have a data problem! The data we DON’T have: ● Large scale, wide geographic range ● Baselines, panel data ● Poverty/HDI/Food consumption surveys detailing fisheries information The data we DO have: ● Case studies (short, different methods, aims, not repeated) ● … and it disappears and is forgotten…
  3. 3. What does this mean for understanding fisheries? ● No synthesized data on basic metrics of poverty and wellbeing, hinders national, regional and global comparisons and IA (no baselines) ● No evidence base for micro-scale economic contribution of fishing (earnings, profits, multipliers) ● Unsubstantiated rhetoric emerges “ … fishing is the occupation of last resort” “… fisherfolk are the poorest of the poor” ● Hinders the case for targeted poverty reduction investment, and the case for reform is based on weak evidence
  4. 4. FishMicroEcon <ul><li>● Micro Economic Database of African Inland fisheries, HH and village surveys </li></ul><ul><li>● 3 main projects </li></ul><ul><li>Food security and poverty alleviation through improved valuation and governance of river fisheries in Africa (WorldFish & BMZ) </li></ul><ul><li>(i) Cameroon </li></ul><ul><li>(ii) Nigeria </li></ul><ul><li>(iii) Malawi & Zambia </li></ul><ul><li>LADDER (UEA, DFID) </li></ul><ul><li>Uganda, Malawi, Kenya </li></ul><ul><li>LSMS-ISA fisheries module pilot survey (WorldFish, World Bank) </li></ul><ul><li>Malawi, Uganda </li></ul>
  5. 5. FishMicroEcon - Synthesis of micro-econ/livelihood case studies <ul><li>Variables identified: </li></ul><ul><li>- Demographic data (HH roster) </li></ul><ul><li>Income data (crops, livestock, fishing, fishing related sources, </li></ul><ul><li>non-farm/fishing, transfers, remittances) </li></ul><ul><li>- Assets (land, livestock, fishing) </li></ul><ul><li>- Material possessions </li></ul><ul><li>- Housing materials </li></ul><ul><li>- Shocks </li></ul><ul><li>- Food security </li></ul><ul><li>- Savings and credit </li></ul><ul><li>- Non-food expenditure </li></ul>Assets by income group, L. Chilwa
  6. 6. FishMicroEcon: The questions Who is poor ? (HH level analysis, with some intra-HH data) What are the assets that define poverty? What are the assets that allow people to get out of poverty? What is the relationship between fishing and agriculture? - e.g. Does fishing improve agricultural productivity?
  7. 7. Research Data Management Project (RDMP) <ul><ul><li>People: </li></ul></ul><ul><ul><li>Dr. Nicolas Bailly, Project Manager of FishBase, NRM @ Los Banos </li></ul></ul><ul><ul><li>Stanley Tan, Senior Systems Analyst, NRM @ HQ </li></ul></ul><ul><ul><li>Beth Timmers, Research Assistant, PESS @ HQ </li></ul></ul><ul><li>Ultimate goals: </li></ul><ul><ul><li>to make ALL data produced by WFC available within and outside the Center, respecting data ownership and sensitivity </li></ul></ul><ul><ul><li>2. to facilitate the exploration of large data sets to promote new methods of global analyses </li></ul></ul><ul><li>These goals are complex; a complete data portal requires additional research, development and funding </li></ul>
  8. 8. Research Data Management Project (RDMP) <ul><li>The specific objectives of the project: </li></ul><ul><ul><li>Phase 1. the ability to retrieve data sets through primary metadata internal website </li></ul></ul><ul><ul><li>Phase 2. the ability to retrieve data sets through a GIS mapping system </li></ul></ul><ul><ul><li>Phase 3. building new datasets from pre-existing data </li></ul></ul><ul><li>The first phase of the project has just begun and is expected to be completed in March 2012 </li></ul><ul><li>There are many challenges... </li></ul>
  9. 9. Research Data Management Project (RDMP) We need help from the WFC scientists to complete phase 1     Your Input   Explanations Dataset Name       Name to describe dataset Dataset Description     Brief description of data Dataset Remarks     Any important comments on data Dataset Publisher     Either WorldFish or WorldFish plus partners Citation Author         Year 2011       Title         Source       Project Code         Project Staff 1     Staff associated with creating database, including project leader   2     3     4     Institutions 1     External institutions associated with dataset, including individual team members involved   2     3     4  
  10. 10. Research Data Management Project (RDMP) Why you want to be involved! 1. Opportunity to do analysis at a larger scale = Exciting science! 2. Greater efficiency of project method design At a glance find out what research has been done in a location, on a topic, or using a methodology, and identify gaps and need, while avoiding reinventing the wheel. E.g. Every HH survey has the same beginning component 3. Improve the quality of our methods using lessons learned from past projects 4. Have greater impact - evidence-based policy
  11. 11. Asanteni Natotela Twalumba Weebale Zikomo
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