Category Archives: oa.search

The open access needle in the discovery haystack | Jisc

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“The transition towards making scholarly communication openly available and accessible for all is a high priority for funding organisations. A tremendous amount of work is underway by libraries to disclose new sets of data to ensure that their collections can be searched and found.

Supporting this trend, Jisc has recently launched the library hub discover offering a fast and convenient way to search for publications held by UK libraries….

There is good news, however! A lot of different people and organisations around the world are focused on finding ways forward….

In addition to all the work that Jisc has done, the OAPEN initiative has been active for some years and the Knowledge Exchange has undertaken surveys and released reports. The British Library is leading on work and, more recently, the COPIM project has received a substantial amount of funding to pursue solutions in this space….

As part of the Jisc library hub roadmap, we have plans to build and release a tool that will help smaller libraries and open access publishers to more easily create good quality bibliographic records that will be optimised for visibility in discovery systems….”

The amount of data brought together by this service is growing daily, but at the time of writing, 135 academic and specialist libraries have contributed, adding up to more than 102 million catalogue records. But here’s the rub: among those 102 million records, it’s not easy to find out which electronic books under which topic are free, unrestricted and legally accessibly. …”

Which Academic Search Systems are Suitable for Systematic Reviews or Meta?Analyses? Evaluating Retrieval Qualities of Google Scholar, PubMed and 26 other Resources – Gusenbauer – – Research Synthesis Methods – Wiley Online Library

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Abstract:  Rigorous evidence identification is essential for systematic reviews and meta?analyses (evidence syntheses), because the sample selection of relevant studies determines a review’s outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments.

This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed and Web of Science. A novel, query?based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analysed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system.

We call for database owners to recognise the requirements of evidence synthesis, and for academic journals to re?assess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.

SUPP.AI by AI2

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“Dietary and herbal supplements are popular but unregulated. Supplements can interact or interfere with the action of prescription or over-the-counter medications. Currently, it is difficult to find accurate and timely scientific evidence for these interactions.

To solve this problem, Supp.AI automatically extracts evidence of supplement and drug interactions from the scientific literature and presents them here….

To find out more about this work, please read our publication….

Supp.AI is a free service of the non-profit Allen Institute for AI….”

Guest post: a technical update from our development team – News Service

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“Here are some major bits of work that we have carried out:

Enhancements to our historical data management system. We track all changes to the body of publicly available objects (Journals and Articles) and we have a better process for handling that.
Introduced a more advanced testing framework for the source code. As DOAJ gains more features, the code becomes larger and more complex. To ensure that it is properly tested for before going into production, we have started to use parameterised testing on the core components. This allows us to carry out broader and deeper testing to ensure the system is defect free.
A weekly data dump of the entire public dataset (Journals and Articles) which is freely downloadable.
A major data cleanup on articles: a few tens of thousands of duplicates, from historical data or sneaking in through validation loopholes, were identified and removed. We closed the loopholes and cleaned up the data.
A complete new hardware infrastructure, using Cloudflare. This resulted in the significant increase in stability mentioned above and allows us to cope with our growing data set (increasing at a rate of around 750,000 records per year at this point).

And here are some projects we have been working on which you will see come into effect over the next few weeks:

A completely new search front-end. It looks very similar to the old one, but with some major improvements under-the-hood (more powerful, more responsive, more accessible), and gives us the capability to build better, cooler interfaces in the future.
Support for Crossref XML as an article upload format. In the future this may also be extended to the API and we may also integrate directly with Crossref to harvest articles for you. We support the current Crossref schema (4.7) and we will be supporting new versions as they come along….”

An Analysis of Search Results from Institutional Repository: Econpapers – Munich Personal RePEc Archive

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Abstract:  The goal of this research is to examine and explore information retrieval process of patrons who access institutional repositories. Repositories are generally hosted by public universities and run by volunteers which allow researchers to submit their draft versions of their manuscripts in pre-print forms. In this study, we analyze using search methods to sort out research papers classified according to their levels of relevance that are available from a repository, and report the pattern of search results as our findings. Our model employs search methods for searching Econpapers which utilize RePEc bibliographic data. Our analysis attempts to highlight how information seekers, scholars and researchers search relevant topics of their interest and how relevant such information is which is retrieved from an institutional repository. This could aid researchers to modify their search processes to obtain better search results from their queries. The goal is to obtain the most relevant documents from online search. We discuss about the methods employed to retrieve information which is most pertinent to the requirements of researchers. A broad implication could be better utilization of time and resources for efficient retrieval of the most relevant documents of interest that could be expected from searching institutional repositories.

How to find and access peer-reviewed studies (for free) | The Logic of Science

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“Let’s start with where to look. You can try simply doing a standard Google search, but odds are that you will get flooded with tons of blogs and websites, and it is a pretty inefficient way to find what you are after. A much better option is to use a database specifically tailored to peer-reviewed literature. There are two major ones that are freely available that I’m going to talk about: Google Scholar and PubMed (there are many others that are behind paywalls, but I am going to assume that most people reading this are not academics and don’t have access to those)….”

How to find and access peer-reviewed studies (for free) | The Logic of Science

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“Let’s start with where to look. You can try simply doing a standard Google search, but odds are that you will get flooded with tons of blogs and websites, and it is a pretty inefficient way to find what you are after. A much better option is to use a database specifically tailored to peer-reviewed literature. There are two major ones that are freely available that I’m going to talk about: Google Scholar and PubMed (there are many others that are behind paywalls, but I am going to assume that most people reading this are not academics and don’t have access to those)….”

Meta and Kopernio partner to provide easy access to full-text scientific articles | Research Information

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“Millions of full-text scientific articles will soon be available through an easy copyright-compliant one-click process, thanks to a new partnership between Kopernio, part of the Web of Science Group and Meta, a free biomedical discovery tool from the Chan Zuckerberg Initiative.

Meta users will have faster, easier access to millions of full-text scientific articles through Kopernio’s free web browser plug-in, giving scientists one-click access to complete versions of the latest relevant research delivered directly in their Meta feed.

Meta users will have the option to install the Kopernio plugin for one-click retrieval of full-text articles that are accessible to them, either by integrating with institutional subscriptions or by offering a copyright-compliant open access alternative. When the version of record is available via institutional subscriptions, librarians and institutions will receive full usage reporting via the original publisher, as Kopernio usage is COUNTER compliant….”

The National Science Foundation Awards scite Competitive R&D Grant to Build Tool to Identify and…

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“scite, Inc. has been awarded a National Science Foundation (NSF) Small Business Innovation Research (SBIR)grant for $224,559 to conduct research and development (R&D) work ondeveloping a deep learning platform that can evaluate the reliability of scientific claims by citation analysis….”