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Aggregating Research papers from Publishers' Systems to Support Text and Data Mining
- 1. Aggregating Research Papers from Publishers’ Systems to Support Text and Data Mining Deliberate Lack of Interoperability or Not? @openminted_eu Dr. Petr Knoth Knowledge Media institute, The Open University United Kingdom @petrknoth
- 2. Goal Achieve seamless harmonised access to full texts of open access research papers originating from thousands of systems around the world for machines to process and extract knowledge from. 2
- 3. What are we doing @openminted_eu - Aggregating full texts of open access research papers from all over the world - Institutional, subject-based open repositories & journals - Publisher systems - Pre-processing millions of research papers, making them ready to text-mine (API, data dumps) - Working with researchers around the world to extract knowledge from these data
- 4. Challenges @openminted_eu - Standardisation (OAI-PMH, ResourceSync, bespoke APIs, nothing, etc.) - Inconsistent implementation of standards (referencing of full-texts from metadata, variation in fields’ semantics, OpenAIRE guidelines/RIOXX, etc.) - Lack of incentives to adopt standards + legal & ethical issues - Scalability (due to in-adequate standards) or bad practices (Robots exclusion, etc.)
- 5. Approach @openminted_eu - Surveying publishers for machine accessibillity of OA content and technically validating their answers - Encouraging providers to follow good practices (validation tools, advocacy) - Implementing connectors to publishers systems - Addressing scalability issues - Pragmatic approach
- 6. Conclusion Seamless access to world’s research papers is needed to enable the creation of text-mining applications that will transform the way we do research. While we have already managed to provide this for millions of research papers, we are still facing a number of technical, organisational, legal and ethical challenges in making seamless machine access to world’s research papers a reality. 6
Notas do Editor
- = don’t say Text and Data Mining (TDM) of research literature has the potential to revolutionise the way we do research. It can improve the ways in which we discover, access, read, disseminate and evaluate research. However, to realise the full potential of text mining scientific data text-miners need seemless unrestricted access to the underlying data.
- With more than 1.5 million new research papers a year and more than 100 million research papers published, there is no one who can read all relevant information in their field. Consequently, Text and Data Mining (TDM) of research literature has the potential to revolutionise the way we do research. It can improve the ways in which we discover, access, read, disseminate and evaluate research. To realise the full potential of text mining scientific information, we need seamless unrestricted access to the underlying data. We need infrastructure for not just people to access papers, but in particulars for machine to be able to read scientific data at scale. This is an essential building block that will make it possible to increase the effectiveness of science, help us to find new treatments, enable businesses to innovate faster, etc. In this lightning talk I will introduce the challenges we are facing in working towards achieving this.
- Include a slide explaining we are specifically looking for publisher platforms