Improve Subject Sorting By Removing Useless Prefixes

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Improve Subject Sorting by Removing Useless Prefixes

Hey everyone! Let's dive into how we can make our subject values way more user-friendly. We're talking about cleaning up those messy strings that clutter our search and browsing experiences. You know, the ones that start with "FOS:" or some random number followed by text. Trust me, getting rid of this junk will make a huge difference!

The Problem: Cluttered Subject Values

So, here's the deal. Many of our subject values are filled with stuff that doesn't really help users find what they're looking for. We've got two main culprits here:

  1. "FOS:" Keywords: These come from the Field of Science Classification System. For example, you might see something like "FOS: Biological sciences." While this might be useful for some internal classifications, it's just extra baggage for the average user trying to browse subjects.
  2. ANZSRC Field of Research Codes: These are those five- or six-digit numbers followed by a text string. Think "69999 Biological Sciences not elsewhere classified." Again, super specific and not exactly helpful for general browsing.

The issue? These prefixes make faceting and browsing subjects a pain. Instead of seeing a clean list of subjects, users have to wade through a bunch of technical jargon. It's like trying to find a needle in a haystack, and nobody wants that.

Why This Matters: User Experience

User experience is everything, guys! We want people to easily find what they need. By removing these prefixes, we can significantly improve how users interact with our subject values. Imagine a clean, concise list of subjects that actually makes sense. That's the dream, right?

Think about it. When someone searches for "Biological sciences," they probably don't care about the "FOS:" part. They just want to see results related to biological sciences. By stripping away the unnecessary prefixes, we make the whole process smoother and more intuitive.

The Impact of Clean Subject Values

  • Better Faceting: Facets become more readable and easier to navigate.
  • Improved Browsing: Users can quickly scan and find relevant subjects.
  • Enhanced Search: Search results are more accurate and relevant.
  • Happier Users: A better user experience leads to increased engagement and satisfaction.

The Solution: Removing the Prefixes

Okay, so how do we fix this mess? The solution is simple: we need to remove those prefixes! We can do this through some data manipulation magic. Basically, we'll write some code to identify and remove the "FOS:" and ANZSRC code prefixes from our subject values. It sounds technical, but trust me, it's doable.

How to Remove the Prefixes

  1. Identify the Prefixes: We need to clearly define the patterns we want to remove. This includes "FOS:" and the specific format of the ANZSRC codes (five or six digits followed by text).
  2. Write the Code: We'll use a scripting language (like Python) to write a script that identifies these prefixes and removes them.
  3. Test the Code: Before we apply the changes to our entire dataset, we need to test it on a small sample to make sure it works correctly.
  4. Apply the Changes: Once we're confident that the code is working, we can apply it to our entire dataset.
  5. Monitor the Results: After applying the changes, we'll need to monitor the results to make sure everything looks good.

Dealing with "Not Elsewhere Classified"

Now, let's talk about that "not elsewhere classified" string. Honestly, I'm not entirely convinced it's all that useful either. It's kind of vague and doesn't really add much value to the subject value. So, should we remove it too?

The Case for Removing "Not Elsewhere Classified"

  • Vagueness: It doesn't provide specific information about the subject.
  • Redundancy: It often repeats information already implied by the subject.
  • Clutter: It adds unnecessary length to the subject value.

The Case for Keeping "Not Elsewhere Classified"

  • Completeness: It might provide a sense of completeness for some users.
  • Specificity: In some cases, it might help to differentiate between similar subjects.
  • Historical Context: It might be part of the established subject terminology.

The Verdict

I'm leaning towards removing it, but I'm open to discussion. What do you guys think? Should we ditch the "not elsewhere classified" string, or should we keep it around? Let me know your thoughts in the comments!

Benefits of Removing the Prefixes and "Not Elsewhere Classified"

Removing these prefixes and the "not elsewhere classified" string offers several significant benefits:

  1. Improved User Experience: The primary advantage is a cleaner, more intuitive interface for users browsing and searching for information. By eliminating unnecessary clutter, users can quickly identify relevant subjects without being bogged down by technical jargon or vague classifications.
  2. Enhanced Data Accuracy: Removing irrelevant prefixes ensures that subject values accurately reflect the content they represent. This leads to more precise search results and more effective organization of information.
  3. Streamlined Data Management: A cleaner dataset is easier to manage and maintain. Removing prefixes simplifies data processing, analysis, and reporting, reducing the risk of errors and inconsistencies.
  4. Increased Efficiency: Users spend less time sifting through irrelevant information, leading to increased efficiency in their research and information retrieval tasks. This is particularly beneficial for researchers, students, and professionals who rely on accurate and accessible data.
  5. Better Faceting and Filtering: Clean subject values improve the effectiveness of faceting and filtering options, allowing users to narrow down their search results with greater precision. This enhances the overall usability of the platform and makes it easier for users to find the information they need.
  6. Enhanced Interoperability: Removing prefixes can improve the interoperability of subject values across different systems and platforms. This ensures that data can be easily shared and exchanged without loss of accuracy or relevance.
  7. Consistent Data Representation: Standardizing subject values by removing prefixes ensures consistent data representation across the platform. This consistency is essential for maintaining data integrity and facilitating effective data analysis.
  8. More Effective Search Algorithms: Search algorithms can perform more effectively when subject values are clean and consistent. Removing prefixes eliminates noise and improves the accuracy of search results, leading to a more satisfying user experience.

Conclusion

Alright, guys, that's the plan! By removing those pesky prefixes and possibly the "not elsewhere classified" string, we can make our subject values way more user-friendly. This will lead to better faceting, improved browsing, and happier users. Let's work together to make this happen! What are your thoughts and ideas around this topic? Share them down below!