Open Access as a Means to Produce High Quality Data
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This document discusses open access to research data. It states that open access can produce higher quality data, publications, and usage of data. It also notes that open access can lead to higher recognition for researchers and more transparency. While some scientists view data as proprietary, open access is meant to have a positive impact by allowing other researchers to reproduce and build upon published work. The document advocates for establishing institutional policies and providing support for open access in order to improve scientific quality and maximize the benefits of research.
![Data publishing!
Quisumbing A, Baulch B (2010) Chronic Poverty and Long Term Impact Study
in Bangladesh <http://hdl.handle.net/1902.1/17045
UNF:5:8MUn92HhwQhRKF69wSTwaA== International Food Policy Research
Institute [Distributor] V5 [Version]>](https://image.slidesharecdn.com/drylandsystemsoct2014gassner-141001021700-phpapp01/85/Open-Access-as-a-Means-to-Produce-High-Quality-Data-10-320.jpg)
Open Access as a Means to Produce High Quality Data
- 1. Open access as a means to produce high quality data Anja Gassner Head Research Method Group Sentinel Landscape Coordinator FTA World Agroforestry Centre (ICRAF)
- 2. The policy is applicable both to new data as well as retrospectively to legacy data: 1. Data shall be made open access as soon as possible and in any event within 12 month of completion of the data collection or appropriate project milestone 2. Existing and future databases shall be made Open access 3. Datasets shall be made open access after the publication the data replicates is published. The consortium policy provides two options that allow centers to decide when and what kind of research data should be made open access 1. data sets that are regarded as not of value to others (draft, poor quality or incomplete) are excepted from this policy (Section 4.1.1. Openness). This option is important if data collection is done by partners and is not in our full control. 2. Completion of data collection is a relative term and independent of funding (unless stated otherwise in the grant contract) and project closure. Thus it is up to the center to define this on a case by case basis and allows control over the actual release date.
- 3. Common Misconceptions • Open Access means that I share all my data • Open Access means that I do not have time to use the data for publications • Open Access means that I will not be recognized for my work • Sharing data means I share all my data
- 4. The “selfish” scientist? “Like too many publicly funded ARIs, some Centre and System-wide programs seem to treat data as proprietary” The CGIAR at 31: An Independent Meta-evaluation of the CGIAR (2004)
- 7. Research Quality When evaluating research a clear distinction should be made between research ‘quality’ (i.e. the relative excellence of academic outputs intended for academic consumption, e.g. journal papers and books) and research ‘impact’ (i.e. the benefits that research outcomes produce for wider society) Unfortunately this division is often confused, a prime example being when journal citation (‘quality’) metrics are incorrectly presented as measures of ‘impact’ (Donovan, 2011).
- 8. • Publications are seldom evaluated based on the technical rigor of the data collection procedures, the completeness of the data and its description, and alignment with existing community standards. • To translate conceptual frameworks into empirical sampling designs takes significant research experience. • Thus producing a high value data set that forms the basis of a high quality scientific publication requires a high level of scientific sophistication, • Writing the paper itself requires a good grasp of language, some understanding of the science you're writing about, and an ability to "translate" technical information into plain English and write about it compellingly (Costandi, 2013).
- 10. Data publishing! Quisumbing A, Baulch B (2010) Chronic Poverty and Long Term Impact Study in Bangladesh <http://hdl.handle.net/1902.1/17045 UNF:5:8MUn92HhwQhRKF69wSTwaA== International Food Policy Research Institute [Distributor] V5 [Version]>
- 12. Open Access – a means to an end Research Data Infrastructure Impact of the Data http://ccafs.cgiar.org/data-management-support-pack
- 13. ICRAF’s Research Data Management Policy 1. Projects are responsible for ensuring that research data is described by appropriated Metadata throughout their lifecycle. Metadata should be incompliance with the Simple Dublin Core requirements, or globally accepted metadata standards for specific data types 2. Every project shall upon closure provide a list of all data sets produced by the project to the regional coordinator and the GRP leaders, who will make recommendation regarding the identification of high value data sets, both to the Centre and our partners. These high value data sets shall be submitted to the institute repository. 3. To improve scientific publications, consensus with scientific peers and public trust in the quality of our research outputs the Centre will provide institutional support to ensure that all necessary raw data will be made public to reproduce or replicate every scientific publication that is based on research data. Scientists are required to submit necessary raw, verified data for every scientific publication in standard file formats.
- 14. Open Access? Open Access is a means to an end • Better quality data • Better quality publications • Higher usage of data (internal & external) • Higher Recognition for “Techis” • More transparency • Better Impact
- 15. Use of data 500 450 400 350 300 250 200 150 100 50 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Other publications Journal secondary data Journal primary data
- 16. Big data and informatics: Enormous amounts of data are being rapidly generated in agri-food systems, from the lab to the field to the retailer, generating opportunities to drive innovation at various points in the value chain. Using high throughput methodologies and systems-based approaches these data can be expertly pooled, structured and mined to tackle complex research questions and identify new areas for research, development and innovation. Better models and data are crucial for developing solutions that will help increase productivity, enhance nutrition, increase resilience to the effects of climate change, and preserve/enhance natural capital.
- 17. Thanks