There are several stages to any academic research project, most of which differ depending on the hypothesis and methodology. Few disciplines, however, can completely avoid the data collection step. Even in qualitative research, some data has to be collected.
Unfortunately, the one unavoidable step is also the most complicated one. Good, high-quality research necessitates a ton of carefully selected (and often randomized) data. Getting all of it takes an enormous amount of time. In fact, it's likely the most time-consuming step out of the entire research project, regardless of discipline.
Four primary methods are employed when data has to be collected for research. Each of these comes with numerous drawbacks, however, some are especially troublesome:
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Manual data collection
One of the most tried-and-true methods is the manual collection. It's almost a foolproof method, as the researcher gets to have complete control over the process. Unfortunately, it's also the slowest and most time-consuming practice out of them all.
Additionally, manual data collection runs into issues of randomization (if required) as sometimes it might be nigh impossible to induce fairness into the set without requiring even more effort than initially planned.
Finally, manual data collection still requires cleaning and maintenance. There's too much room for possible error, especially when extremely large swaths of information need to be collected. In many cases, the collection process is not even performed by a single person, so everything needs to be normalized and equalized.
Existing public or research databases
Some universities purchase large datasets for research purposes and make them available to the student body and other employees. Additionally, due to existing data laws in some countries, governments publish censuses and other information yearly for public consumption.
While these are generally great, there are a few drawbacks. For one, university purchases of databases are led by the research intent and grants. A single researcher is unlikely to convince the financial department to get them the data they need from a vendor, as there might not be sufficient ROI to do so.
Additionally, if everyone is acquiring their data from a single source, that can cause uniqueness and novelty issues. There's a theoretical limit to the insights that can be extracted from a single database, unless it's continually renewed and new sources are added. Even then, many researchers working with a single source might unintentionally skew results.
Finally, having no control over the collection process might also skew the results, especially if data is acquired through third-party vendors. Data might be collected without having research purposes in mind, so it could be biased or only reflect a small piece of the puzzle.
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Getting data from companies
Businesses have begun working closer with universities nowadays. Now, many companies, including Oxylabs, have developed partnerships with numerous universities. Some businesses offer grants. Others provide tools or even entire datasets.
All of these types of partnerships are great. However, I firmly believe that providing only the tools and solutions for data acquisition is the correct decision, with grants being a close second. Datasets are unlikely to be that useful for universities for several reasons.
First, unless the company extracts data for that particular research alone, there may be issues with applicability. Businesses will collect data that's necessary for their operations and not much else. It may accidentally be useful to other parties, but it might not always be the case.
Additionally, just as with existing databases, these collections might be biased or have other issues to do with fairness. These issues might not be as apparent in business decision-making,but could be critical in academic research.
Finally, not all businesses will give away data with no strings attached. While there may be necessary precautions that have to be taken, especially if the data is sensitive, some organizations will want to see the results of the study.
Even without any ill intentions from the organization, outcome reporting bias could become an issue. Non-results or bad results could be seen as disappointing and even damaging to the partnership, which would unintentionally skew research.
Moving on to grants, there are some known issues with them as well. However, they are not as pressing. As long as studies are not completely funded by a company in a field in which it is involved, publishing biases are less likely to occur.
In the end, providing the infrastructure that will allow researchers to gather data without any overhead, other than the necessary precautions, is the least susceptible to biases and other publishing issues.
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Enter web scraping
Continuing off my previous thought, one of the best solutions that a business can provide researchers with is web scraping. After all, it's a process that enables automated data collection (in either raw or parsed formats) from many disparate sources.
Creating web scraping solutions, however, takes an enormous amount of time, even if the necessary knowledge is already in place. So, while the benefits for research might be great, there's rarely a good reason for someone in academia to get involved in such an undertaking.
Such an undertaking is time-consuming and difficult even if we discount all the other pieces of the puzzle — proxy acquisition, CAPTCHA solving and many other roadblocks. As such, companies can provide access to the solutions to allow researchers to skip through the difficulties.
Building up web scrapers, however, would not be essential if the solutions wouldn't play an important part in the freedom of research. With all the other cases I've outlined above (outside of manual collection), there's always the risk of bias and publication issues. Additionally, researchers are then always limited by one or other factors, such as the volume or selection of data.
With web scraping, however, none of these issues occur. Researchers are free to acquire any data they need and specialize it according to the study they are conducting. The organizations involved with the provision of web scraping also have no skin in the game, so there's no reason for bias to appear.
Finally, as so many sources are available, the doors are wide open to conduct interesting and unique research that otherwise would be impossible. It's almost like having an infinitely large dataset that can be updated with nearly any information at any time.
In the end, web scraping is what will allow academia and researchers to enter a new age of data acquisition. It will not only ease the most expensive and complicated process of research, but it will also enable them to break off from the conventional issues that come with acquiring data from third parties.
For those in academia who want to enter the future earlier than others, Oxylabs is willing to join hands in helping researchers with the pro bono provisions of our web scraping solutions.