Skip to main content

Open Access is only part of the picture

Open Access Week has traditionally focussed on Open Access to publications, which has been a catalyst to address the transformation of scholarly communication more broadly.

Our OA Week celebrations included a screening of Paywall: The Business of Scholarship, a very insightful film that reminds us why so many people across the globe believe what a difference Open Access will make to knowledge, getting us closer to an equal society. Many significant figures from the OA movement are included in the film, and it struck me that some are willing the discussion to broaden out, to transform other aspects of research in need of also being open. Terms used to capture the broader need for openness beyond publications include Open Science / Open Knowledge / Open Research. In the interest of keeping this post as inter-disciplinary as possible I'm going to opt for using the phrase Open Research here.

Those who have stepped one foot into a University or research environment knows that there is a great deal of multifaceted work that goes into producing 500-10,000 words of a research paper or monograph. One example of Open Research beyond publications is Open Data. Studies have shown that making the underlying research data open on top of the long-form written output increases citation possibilities by an average of 30%-65%; that, on top of the citation advantage already possible from making the paper Open Access. The figures, of course, are discipline dependent.

Citations aside, Open Data has wider benefits to the research community, not least avoiding the following -
  • overemphasis of results from small samples;
  • selective reporting;
  • statistical errors skewing final results;
  • insufficient documentation of research methods.
Other benefits include - 
  • greatly improved reproducibility of results, and
  • much greater efficiency by discouraging redundant data collection.
Data that is made Open Access via recognised data repositories, such as the University of Leicester's FigShare repository, will be assigned a Digital Object Identifier. (DOI). The DOI is a key component in Open Access infrastructure because it ensures the data can be linked consistently from reference lists and picked up by citation and altmetric tools. In other words, by adopting the identifiers DOI and, as previously covered in this blog, ORCiD across all research administration systems,  researchers could have the potential to evidence more of their research practices.  

Bridging the Boxes by Islam Elsedoudi for opensource.com

Developments in Open Research can draw heavily upon an open publishing framework, let's take research evaluation as one example. Methods of research assessment have received vast amounts of criticism, including an overemphasis on metrics limited to one aspect of scholarship: journal articles. Another valid criticism of the use of metrics in evaluation could be that it lessens the attention on qualitative assessment and peer-review. One solution to the research evaluation issue is Open Evaluation / Open Peer Review, i.e. an ongoing post-publication process of transparent peer-review and rating of papers. It's thought that Open Peer Review could enable an academic to receive credit for their contributions reviewing a significant paper in their field.

Another example deserving more standardised recognition is expertise on a particular resource, collection or equipment. ORCiD have expanded the data collected in an ORCiD profile to include a research resources section, which aims to enable researchers to make their resource expertise a discoverable entity to employers, funders and future collaborators. 

The tools covered in this short series of OA Week blog posts are designed with a bigger picture in mind: an Open Research infrastructure that can really come into its own once they are applied to all aspects of scholarship.

Popular posts from this blog

You can now export multiple citations from Google Scholar

You can now export multiple citations from Google Scholar if you have a Google Account. Go to Google Scholar and sign into your Google Account. Conduct your search. Click on the Star icon (Save) under each reference you want to export. Then click on My Library in the top, right of the screen. Select all the references and click on the Export option: Click the Star/Save Icon Choose Export Option To Export into EndNote Choose the EndNote option. Open the EndNote file that is created. The references should automatically import into EndNote. To Export into RefWorks Choose the RefMan option. Save the RIS file that is created. Login to your RefWorks account. Click on the plus (+) button. Choose Import References. Add the RIS file you just saved. Set the file import option to RIS - Reference Manager. Click import and your references will be imported. --- Good Practice Tip: Always check that all the reference information you need has been

Searching ABS Journals in Business Source Premier

In Business and Management Studies, researchers undertaking a literature review sometimes search across a defined group of journals. This is a way of focusing the literature search to make the results more relevant to the questions in hand. Groups are often chosen from the Association of Business Schools (ABS)'s  Academic Journal Guide . Read more how about how they put together the guide here . There are several ways to search across ABS journals. Here is how to do it in Business Source Premier, a leading literature database for this subject area.  1.     Login into the ABS journal guide. If you have never used it before you will need to create an account. 2.     You can use the guide to draw up a group of journals either by using the Rankings information or the Fields. Fields divides up the journals into categories of research focus e.g. Accounting, Finance etc. In this example we will use the Fields. The field we are interested is ‘Operations Research and Marketin

Advanced Search Tip: Proximity (Adjacency) Searching

Proximity (Adjacency) Searching vs Phrase Searching When you're searching literature databases you might want to find a phrase. The easiest way to do this is to put the phrase in "speech marks". E.g. "heart disease" This will find that exact phrase - with the words next to each other in that order. BUT... You may be interested in variations on that phrase e.g. heart disease, disease of the heart, diseases of the heart, diseases of the human heart. In that case it might be better to use a proximity/adjacency search - this allows you to find one keyword next to another. Or one keyword within a specified number of words of the other keyword. When using a proximity search the keywords can be in any order. Different Databases Use Different Proximity Operators In Ovid Medline : heart adj disease finds the word heart next to the word disease, in that order.    (This is the same as searching for the phrase, of course) heart adj2 disease fin