1 / 23

What is data?

What is data?. Wietse Dol, LEI-WUR (W.Dol@wur.nl) 13 November 2012, 9.40 – 10.25, C435 Forumgebouw. LEI: Agricultural Economic Research Institute. Part of Wageningen University & Research center (WU R )

turner
Download Presentation

What is data?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. What is data? Wietse Dol, LEI-WUR (W.Dol@wur.nl) 13 November 2012, 9.40 – 10.25, C435 Forumgebouw

  2. LEI: Agricultural Economic Research Institute • Part of Wageningen University & Researchcenter (WUR) • Part of the Social Science Group within the WUR • We are the research part of WUR/SSG (advice ministry of Agriculture) in The Hague • Consultancy (applied research): ministries, EU, local government, industry,… • Collecting data (Farm data: FADN), building models and agricultural content specialists

  3. University vs. Research center • University: teaching, publications, new theory and technology • Research center: • applied work/consultancy • reusing things from the past (e.g. yearly publications) • sharing knowledge (how to become a content specialist)/teaching for small groups • working in groups (different disciplines) • Working in (inter)national groups with many different disciplines

  4. Wietse Dol • PhD Econometrics • 10 years University of Groningen (Econometrics, sampling theory) 18 years LEI (many different departments) • Data and models, i.e. use/reuse and quality, trouble shooter + statistical methods + ICT + user interfacing • Not and IT guy but a researcher (I build software because I use it myself) • Many model projects and user interfaces for models (not only LEI) • Currently: data, data quality, …

  5. Data, lifecycle and data management http://datalib.edina.ac.uk/mantra/researchdataexplained.html http://www.dcc.ac.uk/resources/curation-lifecycle-model http://www.data-archive.ac.uk/create-manage/life-cycle

  6. Data is anything and everything Research data: collected, observed, or created, for the purpose of analysis to produce and validate original research results. Anything can become the interest of research … Research Data

  7. Primary v.s. Secondary data • Primary data: you collect, targeted to answer/validate your questions. • Secondary data: not yours. • Quality of data • Meta-information is crucial • More and more need of secondary data (primary is expensive and takes a lot of time to collect).

  8. Production data Meta-information: Source, Version, Dimension, Definitions etc. without proper information you use the wrong data • is FR with or without DOM? • Is the production in tons or in Euros. • Does the year start 1-1 and ends 31-12? • What’s the definition of Tomato

  9. DCC Curation Lifecycle Model

  10. CREATE & MANAGE DATA: RESEARCH DATA LIFECYCLE

  11. Data • How to get the data, filter it and store it • Quality checks on the data • How to make it available for others • What scientific actions are done on the data • Curate, preserve, versions, ..

  12. Types of databases according MetaBase • Statistical database • Scientific database • Meta-database

  13. Statistical database • Databases provided by international organizations like EU, FAO and OECD are in general statistical databases: • Data are stored as they are received • Data are consistent in their own domain • No aggregations are made when underlying data are missing • Not much attention for data checking

  14. Scientific versus Statistical database • Problems with statistical database: • Different definitions of territories and commodities • Typing errors • Missing data • Break in series • Scientific database: • Problems solved • Transparency (original data sources and underlying assumptions are kept) • Essential for modeling and research

  15. Structural design of a scientific database • Key words for structural design HarDFACTS project IPTS 2007 done by vTI/LEI • Transparent • Harmonised • Complete • Consistent Harmonised Database for Agricultural Commodity Time Series

  16. Transparent • Original data from statistical database are stored • Complete and consistent data are stored • Original and completed data can be compared • Calculation procedures are stored and can be repeated

  17. Harmonised • Definition used here is to bring together the different international databases in one framework and to link the data through a unique coding system (keywords are classifications and tree structures)

  18. Complete • Definition used in MetaBase is that an econometric procedures will be proposed to complete the new (time) series in the database. • Trend estimates • Interpolation • Correlation and regression with other variables (e.g. TRAMO: Time series Regression with Arima noise, Missing observations and Outliers)

  19. Consistent • Definition used here is that the inter relationship of the data in the database holds over classifications (time, territories and variables).

  20. MetaBase

  21. MetaBase many different data sources (e.g. FAO, Eurostat) all in same user-interface (SDMX, NetCDF) find data alternatives using Meta-Information search data content (e.g. oilseed) all content easily available in research software (R/GAMS) recodings, aggregations and concordances are all implemented in GAMS Statistical methods in GAMS and R

  22. Thank you for your attention! Or send an email: Wietse.Dol@wur.nl

More Related