DOCSLIB.ORG

The OECD DELTA Project – Making OECD Statistical Data Open

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The OECD DELTA Project – Making OECD Statistical Data Open The OECD DELTA Project – making OECD statistical data open Trevor Fletcher OECD, e-mail: [email protected] Abstract The OECD is currently undertaking a project (‘DELTA’) with the aim of making its statistical data open, accessible and free. In the context of this project, ‘open’ means that data content is machine-readable, retrievable, indexable and re-usable. This paper provides background to the DELTA project and describes the steps being taken to implement an Application Programming Interface (API) to provide machine-to- machine access to the OECD statistical data warehouse “ OECD.Stat ” via a number of formats along with the challenges involved in standardising the statistical content from the 800+ datasets. The paper describes the steps being put in place to encourage re-use of OECD data and re-use by OECD of external innovation through Open innovation process and community. The paper examines links to the related DELTA project work streams to make data accessible and free and links to the Knowledge Information Management (KIM) Ontology Management and Semantic Infrastructure project via linked data. Keywords : OECD.Stat , API, Web Services 1. Background to the OECD DELTA Project Statistics are of strategic importance to the OECD both as an input for internal analysis and also as a product for dissemination to a wider audience in their own right. Taking into account the importance of statistics as a publication output, a review of the OECD Publishing Policy was carried out during 2011. Among other issues, the review covered the following points related to the dissemination of statistics: • An assessment of OECD’s publishing policy benchmarked against that of other International Organisations • Technologies used in disseminating publications and data • An assessment of the quality of publications and the services provided • The overall accessibility of OECD datasets Following the Publication Policy report a number of recommendations were proposed to make OECD statistics “open, accessible and free”. The OECD Council welcomed this proposal and as a result the DELTA project was initiated to implement these aims. The DELTA Project was given the objective to deliver on the Council decisions to “increase dissemination and usage and to maintain a viable, sustainable cost recovery model”. Subsequently 3 project work streams were established: “Open”, “Accessible” and “Free”. These can be defined as follows: • Accessible : Develop a new, more user friendly, statistical portal “data.oecd.org”, as a single, central gateway to access all OECD statistical data. • Open : Provide open data services for OECD statistical data. • Free: Make all data freely available. This paper describes the “Open” work stream. 2. DELTA Project – Open Data Openness is one of the key values that guide the OECD vision for a stronger, cleaner and fairer economy. Making data open is an important part of this and to this end a number of open benchmarks in the project have been defined as follows: • Completeness – content should include data, metadata, sources and methods. • Primacy – datasets should be primary and not aggregated and include details on how the data was collected. • Timeliness – data should be automatically available in trusted third-party repositories upon publication. • Ease of access – data made available via a simple Application Programming Interface (API) • Machine Readability – data and metadata provided in machine-readable standard plus documentation. • Non-discrimination – No special permissions required to access data. • Use of common standards – Stored data can be accessed without a special software license. • Licensing – Creative Commons CC-BY (Licensees may copy, distribute, display and perform the work and make derivative works based on it only if they give the author or licensor the credits in the manner specified by these). • Permanence – Information made available remain online with archiving over time together with notification mechanism. • Usage costs – Free. 2.1 Open Data Project goals Data today can be extracted only via downloads from OECD.Stat. The ODWS will make them available to other web sites directly for creating custom data visualizations, live combinations with other data sources etc. The goals of the Open Data project are: to make OECD data machine-readable, retrievable, indexable and re-usable; to increase the dissemination and impact of OECD data via open data services for its statistical data; and, to encourage re-use of OECD data by external innovation communities, In terms of deadlines, the first version of the Open Data project providing a sub-set of OECD data should be available by Q1 2014. The full DELTA version of Open Data will be available by Q2 2015. The DELTA Open Project has 3 main deliverables: i) a full set of “Open-ready” data and metadata; ii) a set of Open Data Web Services and iii) an interface for managing the OECD Open Innovation Community. 2.2 “Open-Ready” Data and metadata. For data to be considered “Open-ready” the existing data and metadata content of the OECD corporate data warehouse OECD.Stat will be required to meet certain criteria of structure and content necessary for machine-to-machine access. The criteria for Open- readiness include: • OECD.Stat data content must include units of measure and scale which are structured and consistent across datasets. • Variable labels must be sufficiently clear when taken out of context of the full dataset such that they are self-explanatory. • All datasets must have structured statistical metadata to help users understand the data • Data should be stored at unit level (actual values) • Common dimensions (Time, Country etc) should be used • Common dimension names (i.e. “Subject”) should be used To achieve this, data owners will carry out a self-assessment of all OECD.Stat data content to gauge the state of open-readiness for each dataset. This will involve analysing the metadata content according to the criteria. 2.3 Open Data Web Services (ODWS) In parallel to the data assessment exercise, the Open Data Web Services will be developed. This will involve building a set of Web Services to provide machine-to- machine access to OECD.Stat data via a number of formats. In addition an interface to manage user registration and access to the Web Services will be designed and developed as part of the OECD.Data.Org statistics portal. This will involve defining the technical standards for data to be machine-readable that meet the needs of both expert and non-expert audiences. Application Programming Interfaces (API) will be developed to make the data and metadata in OECD.Stat available to systems outside the organisation via a number of formats. These services will be made available to the public via an Open Data Interface (ODI) together with the necessary technical and other documentation for developers. 2.4 Open Data formats. Data and metadata will be made available to external users in as many output formats as possible to maximise data access. The project will start with formats including: SDMX/JSON, Restful API, OData, XLS and CSV. Additional formats will be added as needed over time. These formats have been chosen for the reasons described below. a) Excel/CSV Excel and CSV are already widely used exchange standards so including them as output formats was a fairly obvious decision. b) SDMX/JSON JavaScript Object Notation (JSON ) is a text-based open standard designed for human-readable data interchange and has become one of the most popular industry-used open data formats on web sites today. JSON has a number of advantages, including: • Simplicity - JSON is a simple and ‘lightweight’ format with a smaller grammar and can map directly onto the data structures used in modern programming languages. • Interoperability - JSON has the same interoperability potential as XML. • Openness - JSON has the same open capabilities as XML • Readability - JSON is much easier for human to read than XML. It is easier to write and is easier for machines to read and write. The Statistics Data and Metadata eXchange standard ( SDMX ) provides a standard model for statistical data and metadata exchange between national agencies and international agencies, within national statistical systems and within organisations. OECD is a member of the SDMX Sponsor Group (together with the Bank of International Settlements, European Central Bank, Eurostat, International Monetary Fund, United Nations Statistics Division and World Bank). SDMX data extracts from OECD.Stat are already provided via a web service; this will be adapted as an API using the SDMX compact version. c) Open Data (OData) OData is an open protocol for sharing data Future formats could include Google Data (a REST-inspired technology), Google Dataset Publishing Language ( DPSL ) or Google KML , a Geospatial file format. An interface will be developed to allow users to access the Open Data Web Services and associated technical documentation and to register their intention to use the data and sign a Terms of Agreement. 3. Linked Data and the OECD KIM project The OECD Knowledge and Information Management (KIM) has been established to integrate information and centralise access to all OECD content (corporate content management, record management, authoring, etc.). KIM was launched in parallel to the DELTA project and is concerned with developing semantic enrichment and centralized taxonomy linked data support. A long-term goal of the project is to create linked data sources with the Resource Description Framework (RDF)
Load more

Recommended publications
  • The Statistical Data and Metadata Exchange Standard (SDMX)
    UNITED NATIONS ECONOMIC AND SOCIAL COMMISSION FOR ASIA AND THE PACIFIC Expert Group Meeting: Opportunities and advantages of enhanced collaboration on statistical information management in Asia and the Pacific 20-22 June 2011 Swissotel Nailert Park, Bangkok Tuesday, 21 June 2011 Session 3: Emerging generic business process models and common frameworks and terminology – A basis for practical cooperation? Integrating statistical information systems: The Statistical Data and Metadata eXchange Standard (SDMX) Brian Studman Australian Bureau of Statistics [email protected] Session Overview •SDMX – Background –History – What can it represent – SDMX & Other Standards (particularly DDI) • ABS & SDMX: Integration via IMTP (ABS major business programme) • Using SDMX in the context of GSBPM • Examples of Use: ABS main projects that utilise SDMX • A quick preview of GSIM v 0.1 (if we have time) 1 SDMX • Origins – Bank for International Settlements, European Central Bank, Eurostat, IMF, UN, OECD, and the World Bank (SDMX Consortium) • Format for statistical returns for aggregated data • Version 2.1 (April 2011 public comment) • http://sdmx.org/ (best starting place) • SDMX comes from the international agencies (OECD, IMF, Eurostat, UNSD, World Bank, ECB, BIS) – they get aggregate statistical tables from many countries regularly over time – they wanted to automate and manage the process • they need standard agreed definitions and classifications, standard agreed table structures, standard agreed formats for both data and metadata – They commissioned
    [Show full text]
  • Joint Adb – Unescap Sdmx Capacity Building Initiative Sdmx Starter Kit for National Statistical Agencies
    JOINT ADB – UNESCAP SDMX CAPACITY BUILDING INITIATIVE SDMX STARTER KIT FOR NATIONAL STATISTICAL AGENCIES Version: 5 October 2014 1 FOREWORD The aim of the Starter Kit is to provide a resource for national statistical agencies in countries in the Asia and Pacific region contemplating the implementation of the Statistical Data and Metadata Exchange (SDMX) technical standards and content-oriented guidelines for the exchange of aggregate data and their related methodological information (metadata). It outlines a structured process for the implementation of SDMX by agencies that have little knowledge about SDMX and how they would go about the implementation of the standards and guidelines. In order to avoid duplicating the work of the SDMX sponsoring agencies the Kit makes extensive use of links to existing SDMX background documents and artifacts that have been developed at the global level. The Starter Kit was developed under the auspices of the joint 2014 Asian Development Bank– United Nations Economic and Social Commission for Asia and the Pacific SDMX initiative to improve the efficiency of data and metadata exchange between national statistical agencies in the Asia and Pacific region and international organizations through the ongoing use of SDMX standards. The aim of the joint initiative is to build the capacity of countries in the region to apply SDMX standards through mapping national concepts to specific identified SDMX Data Structure Definitions (DSDs) and Metadata Structure Definitions (MSDs). The initiative also aims at enabling national agencies to determine which, of a range of available tools for SDMX implementation, best meets the needs of the organisation. The basic premise of the Kit is that SDMX implementation must be seen in the context of a wide range of corporate institutional, infrastructure and statistical initiatives currently underway in almost all statistical agencies around the globe to improve the quality and relevance of the service they provide to government and non-government users of their outputs.
    [Show full text]
  • Central Banks' Use of the SDMX Standard
    Irving Fisher Committee on Central Bank Statistics IFC Report No 4 Central banks’ use of the SDMX standard 2015 Survey, conducted by the SDMX Global Conference Organising Committee (this report includes only the central bank responses) March 2016 1 Contributors to the IFC report Bank for International Settlements (BIS) Heinrich Ehrmann Bruno Tissot (IFC Secretariat) Central Bank of the Republic of Turkey (CBRT) Erdem Başer Timur Hülagü This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2016. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISSN 1991-7511 (online) ISBN 978-92-9197-480-1 (online) 1 The views expressed in this document reflect those of the contributors and are not necessarily the views of the institutions they represent. Contents 1. Executive summary .......................................................................................................................... 1 2. Background: SDMX ......................................................................................................................... 3 3. Survey findings .................................................................................................................................. 5 Annex A: List of participating central banks and respondents ........................................... 12 Annex B: IFC Questionnaire on central banks’ use of SDMX standard ............................ 14 Annex C: SDMX Roadmap 2020 .....................................................................................................
    [Show full text]
  • Implementor's Guide for Sdmx Format Standards (Version
    STATISTICAL DATA AND METADATA EXCHANGE INITIATIVE IMPLEMENTOR’S GUIDE FOR SDMX FORMAT STANDARDS (VERSION 1.0) STATISTICAL DATA AND METADATA EXCHANGE INITIATIVE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Initial Release September 2004 26 First Revision December 2004 27 © SDMX 2004 28 http://www.sdmx.org/ 29 30 31 32 33 3 STATISTICAL DATA AND METADATA EXCHANGE INITIATIVE 34 1 INTRODUCTION ............................................................................................................................ 5 35 2 SDMX INFORMATION MODEL FOR FORMAT IMPLEMENTORS .............................................. 5 36 2.1 Introduction...................................................................................................................................................5 37 2.2 Fundamental Parts of the Information Model .................................................................................6 38 2.3 Data Set.....................................................................................................................................................6 39 2.4 Attachment Levels and Data Formats....................................................................................................8 40 2.5 Concepts, Definitions, Properties and Rules.......................................................................................9 41 3 SDMX-ML AND SDMX-EDI: COMPARISON OF EXPRESSIVE CAPABILITIES AND 42 FUNCTION .........................................................................................................................................
    [Show full text]
  • SDMX 2.1 User Guide Aims at Providing Guidance to Users of the Version 2.1 of the Technical Specification, Released in April 2011
    EUROSTAT – SDMX Training – March 2015 UNDERSTANDING SDMX Introduction to a training course March 2015 1 | P a g e EUROSTAT – SDMX Training – March 2015 Table of Contents 1 SDMX in short ................................................................................................. 3 2 Why should you be interested in SDMX? ........................................................... 4 3 Background: origin and purpose of SDMX .......................................................... 5 4 The existing versions of SDMX .......................................................................... 8 5 The SDMX information model .......................................................................... 9 6 Uptake of SDMX within Domains .................................................................... 10 7 How to know more: SDMX Tutorials ............................................................... 12 8 How to know more: the SDMX User Guide ...................................................... 12 Marco Pellegrino (Eurostat) March 2015 2 | P a g e EUROSTAT – SDMX Training – March 2015 What is SDMX This document provides some background information on the SDMX Initiative, the issues which SDMX addresses and the areas in which SDMX is playing a role today. It also provides a description of a training course on the Basics of SDMX, scheduled for GCC-Stat. 1 SDMX in short SDMX provides support for things that are important to statisticians and are often difficult for them to achieve, and it enables tools to be developed to provide support for these things. It does this by providing standard, well-designed formats for holding all of the elements involved in the statistical process and linking them all together in a clear model. The result is an approach that maximises the amount of metadata and statistical context information that can be passed through to statistical clients, maximises the possibility of relating statistics from similar or different sources, and allows for the automation of processes that are often difficult and costly to manage otherwise.
    [Show full text]
  • SDMX Roadmap 2021-2025
    SDMX Roadmap 2021-2025 Executive Summary The Statistical Data and Metadata eXchange (SDMX) is a standard developed to facilitate the exchange of data and metadata in the international statistical community. It is widely used by international organizations, national statistical offices, and other data producing agencies. The development of SDMX is guided by a roadmap that sets forth the objectives for a span of five years. The SDMX Roadmap 2021-2025 (“the Roadmap”) provides a strategic plan for the continuous development of SDMX over the next five years. The Roadmap has two main drivers: addressing a broadening set of needs and improving the usability of the standard. These two drivers come together under the umbrella of the SDMX 3.0 project. The new roadmap continues to group the objectives into four pillars: (i) Implementation, (ii) Simplification, (iii) Modernisation and (iv) Communication. The Roadmap aims to strengthen the implementation of SDMX. SDMX will be expanded to new statistical domains. Existing SDMX packages will be upgraded based on updates of international classification standards. The adoption of established global code-lists and data structure definitions (DSDs) will be encouraged. Finally, SDMX tools will be finalized to facilitate the exchange of referential metadata. Simplification will be required to facilitate the use of SDMX. Main directions of work will be to simplify referential metadata exchange mechanisms, explore links between SDMX and other standards, and develop tools that simplify the complexities of SDMX for novice users. SDMX will be leveraged to modernise statistical processes and IT infrastructure. The development of SDMX 3.0 will incorporate modern features of data management.
    [Show full text]
  • Policy-Making for Research Data in Repositories: a Guide
    Policy-making for Research Data in Repositories: A Guide http://www.disc-uk.org/docs/guide.pdf Image © JupiterImages Corporation 2007 May 2009 Version 1.2 by Ann Green Stuart Macdonald Robin Rice DISC-UK DataShare project Data Information Specialists Committee -UK Table of Contents i. Introduction 3 ii. Acknowledgements 4 iii. How to use this guide 4 1. Content Coverage 5 a. Scope: subjects and languages 5 b. Kinds of research data 5 c. Status of the research data 6 d. Versions 7 e. Data file formats 8 f. Volume and size limitations 10 2. Metadata 13 a. Access to metadata 13 b. Reuse of metadata 13 c. Metadata types and sources 13 d. Metadata schemas 16 3. Submission of Data (Ingest) 18 a. Eligible depositors 18 b. Moderation by repository 19 c. Data quality requirements 19 d. Confidentiality and disclosure 20 e. Embargo status 21 f. Rights and ownership 22 4. Access and Reuse of Data 24 a. Access to data objects 24 b. Use and reuse of data objects 27 c. Tracking users and use statistics 29 5. Preservation of Data 30 a. Retention period 30 b. Functional preservation 30 c. File preservation 30 d. Fixity and authenticity 31 6. Withdrawal of Data and Succession Plans 33 7. Next Steps 34 8. References 35 INTRODUCTION The Policy-making for Research Data in Repositories: A Guide is intended to be used as a decision-making and planning tool for institutions with digital repositories in existence or in development that are considering adding research data to their digital collections.
    [Show full text]
  • Information Model for Format Implementers
    SDMX STANDARDS: SECTION 6 SDMX TECHNICAL NOTES VERSION 2.1 April 2011 © SDMX 2011 http://www.sdmx.org/ Contents 1 Purpose and Structure.....................................................................................1 1.1 Purpose..............................................................................................................1 1.2 Structure.............................................................................................................1 2 General Notes on This Document...................................................................1 3 Guide for SDMX Format Standards ................................................................2 3.1 Introduction.........................................................................................................2 3.2 SDMX Information Model for Format Implementers...........................................2 3.2.1 Introduction....................................................................................................2 3.3 SDMX-ML and SDMX-EDI: Comparison of Expressive Capabilities and Function .......................................................................................................................3 3.3.1 Format Optimizations and Differences ..........................................................3 3.3.2 Data Types ....................................................................................................4 3.4 SDMX-ML and SDMX-EDI Best Practices .........................................................6 3.4.1 Reporting and Dissemination
    [Show full text]
  • Annex 1 Technology Architecture 1 Source Layer
    Annex 1 Technology Architecture The Technology Architecture is the combined set of software, hardware and networks able to develop and support IT services. This is a high-level map or plan of the information assets in an organization, including the physical design of the building that holds the hardware. This annex is intended to be an overview of software packages existing on the market or developed on request in NSIs in order to describe the solutions that would meet NSI needs, implement S-DWH concept and provide the necessary functionality for each S-DWH level. 1 Source layer The Source layer is the level in which we locate all the activities related to storing and managing internal or external data sources. Internal data are from direct data capturing carried out by CAWI, CAPI or CATI while external data are from administrative archives, for example from Customs Agencies, Revenue Agencies, Chambers of Commerce, Social Security Institutes. Generally, data from direct surveys are well-structured so they can flow directly into the integration layer. This is because NSIs have full control of their own applications. Differently, data from others institution’s archives must come into the S-DWH with their metadata in order to be read correctly. In the early days extracting data from source systems, transforming and loading the data to the target data warehouse was done by writing complex codes which with the advent of efficient tools was an inefficient way to process large volumes of complex data in a timely manner. Nowadays ETL (Extract, Transform and Load) is essential component used to load data into data warehouses from the external sources.
    [Show full text]
  • PDF Presentation
    Dexterity: Data Exchange Tools and Standards for Social Sciences Louise Corti, Herve L’Hours, Matthew Woollard (UKDA) Arofan Gregory, Pascal Heus (ODaF) I-Pres, 29-30 September 2008, London Introduction to DExT data exchange models and data conversion tools for primary research data two aims: z a standard format for representing richly encoded qualitative data z conversion tools from proprietary statistical software to open formats Project Environment JISC funded funded by JISC (Joint Information Systems Committee) Capital Programme: Repositories and Preservation (Tools and Innovations strand) small budget for one year – proof of concept Project Environment UKDA z the leading UK social science data archive z documenting, preserving and disseminating data for over 40 years z pioneered the archiving and sharing of qualitative data at national level z commitment to open standards and formats z support to DExT project through testing, maintaining and embedding conversion tools and standards 1. DExT: qualitative data data selected were from the social sciences ‘qualitative’ data: annotated and coded text, multimedia, linked sources z in-depth and semi-structured interviews z focus groups, observations, field notes, case study notes z diaries, open-ended survey questions z personal documents and photographs the data formats are typically found across all domains of primary research ESDS Qualidata z national service providing access to and support for a range of social science qualitative datasets z part of the ESRC funded Economic and Social
    [Show full text]
  • (I) Functions of Metadata in Statistical Production
    The SDMX Istat Framework. Prepared by Mauro Bianchi, Dario Camol and Laura Vignola, ISTAT, Italy I. INTRODUCTION ISTAT in the two last years have been involved in several Eurostat SDMX projects the Eurostat SODI project, "Demography Pilot Project" and recently to the "Census Hub Pilot Project". The experience acquired during those projects has brought the development team in building the SDMX Framework. The idea was to create a new environment to manage the entire data flow that conducts to the reporting of data and reference metadata in SDMX format. The SDMX Framework, released as open source package, is composed by building blocks that can be also used separately, so that other statistical agencies can re-use and integrate them in their environments II. ARCHITECTURE A. The data framework The framework is composed by modules and classes that permit the user to create the environment, load data, create SDMX data file in compact and cross sectional format and the corresponding RSS (pull method without a web service) and make available data by a web service. 1.2 Manager The application Manager is a Client-Server application that creates the structure of the database. This structure is “SDMX compliant” that is the tables created inside the database contains as object the elements belong to the information model of SDMX (concepts, descriptors, code lists, key families). These tables are populated using the structure files, containing the Data Structure Definitions and all the information contained in these files are stored in specific tables created in the first loading and updated in every future loading if necessary.
    [Show full text]
  • The SDMX Service Architecture for the Perspective of a National Statistical
    Meeting of the Joint OECD/UNECE Expert Group on Statistical Data and Metadata Exchange 8-9 March Palais des Nations, UNECE, Geneva, Switzerland The SDMX service architecture for the perspective of a National Statistical Institute Prepared by [Francesco Rizzo, ISTAT Italy; Adam Wronski, Eurostat; Bengt-Ake Lindblad, Eurostat] I. Introduction 1. For several years the SDMX Sponsor Organisations have been developing software and tools in order to facilitate the introduction of SDMX in their countries. 2. Eurostat has been discussing the effectiveness of these tools with the Member States (MSs) during task forces meetings and working groups. Many suggestions from the MSs were collected which led in 2009 to an internal project named "SDMX implementation and support for Member States". The aim of that project was to develop a set of software, organized in re-usable building blocks and tools to help MSs in implementing an SDMX service infrastructure for a more efficient participation to some SDMX projects currently running within the European Statistical System (e.g. European Census Hub project). 3. A prototype was presented during the "Statistics, Telematic Networks & EDI" Working Group (STNE) in June 2009. The participants welcomed with enthusiasm the initiative and encouraged Eurostat to proceed forward. 4. The main objectives of the project can be listed as follows: (a) To support for the Census Hub project and other Eurostat projects; (b) To facilitate SDMX implementations within the MSs with a particular attention to the large PC- AXIS community; (c) To stimulate a "SDMX community of developer". 5. The project deliverables consist essentially of: (a) the SDMX NSI Reference infrastructure document.
    [Show full text]