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There are several stages in any academic research project, most of which differ in hypothesis and methodology. Few disciplines, however, can completely avoid the data collection step. Even in qualitative research, some data must be collected.
Unfortunately, the only inevitable step is also the most complicated. Good, high-quality research requires lots of carefully selected (and often random) data. Achieving it all takes an enormous amount of time. In fact, it is likely to be the most time-consuming step of the entire research project, regardless of discipline.
Four main methods are used when collecting data for research. Each has numerous drawbacks, but some are particularly problematic:
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Manual data collection
One of the most tried and true methods is manual picking. It is almost a foolproof method, since the researcher gets to have complete control over the process. Unfortunately, it is also the slowest and most time-consuming practice of all.
Additionally, manual data collection has problems with randomization (if needed), as it can sometimes be nearly impossible to induce fairness in the pool without requiring even more effort than initially anticipated.
Finally, manual data collection still requires cleaning and maintenance. There is too much room for potential error, especially when large amounts of information need to be collected. In many cases, the collection process is not even carried out by a single person, so it is necessary to standardize and equalize everything.
Existing public or research databases
Some universities purchase large data sets for research purposes and make them available to students and other employees. Also, due to existing data laws in some countries, governments release censuses and other information annually for public consumption.
While they are generally great, there are some drawbacks. On the one hand, university database purchases are driven by research intent and grants. A single researcher is unlikely to convince the finance department to get them the data they need from a vendor, as there may not be enough ROI to do so.
Also, if everyone gets their data from a single source, this can lead to problems of uniqueness and novelty. There is a theoretical limit to the knowledge that can be extracted from a single database, unless it is continuously renewed and new sources are added. Even then, many researchers working with a single source could inadvertently bias the results.
Finally, not having control over the collection process can also bias the results, especially if the data is acquired through third-party providers. Data can be collected without regard to research purposes, so it could be biased or only reflect a small piece of the puzzle.
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Obtaining data from companies
Companies have begun to work more closely with universities today. Now, many companies, including Oxylabs, have developed partnerships with numerous universities. Some companies offer subsidies. Others provide tools or even entire datasets.
All of these types of partnerships are great. However, I strongly believe that providing only the tools and solutions for data acquisition is the right decision, with grants second. Datasets are unlikely to be that useful to universities for several reasons.
First, unless the company is extracting data just for that particular research, there may be issues of applicability. Companies will collect the data necessary for their operations and not much else. It may be useful to other parties, but it may not always be the case.
Also, as with existing databases, these collections may be biased or have other fairness issues. These issues may not be as obvious in business decision making, but they can be critical in academic research.
Finally, not all companies will give away data without restrictions. Although the necessary precautions may need to be taken, especially if the data is sensitive, some organizations will want to see the results of the study.
Even without any ill intent on the part of the organization, bias in reporting results could become a problem. Non-results or poor results could be seen as disappointing and even harmful to the partnership, which would inadvertently distort the research.
Moving on to grants, there are also some known issues. However, they are not that urgent. As long as the studies are not fully funded by a company in a field in which it is involved, editorial bias is less likely to occur.
In the end, providing the infrastructure that allows researchers to collect data without any overhead, other than necessary precautions, is the least susceptible to bias and other publication problems.
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Enter web scraping
Continuing with my earlier thought, one of the best solutions a company can offer researchers is web scraping. After all, it is a process that allows the automated collection of data (in raw or analyzed form) from many different sources.
Building web scraping solutions, however, requires an enormous amount of time, even if the necessary knowledge is already in place. So while the benefits to research can be great, there is rarely a good reason for someone in academia to get involved in this venture.
This undertaking is time-consuming and difficult, even if we discount all the other pieces of the puzzle: proxy acquisition, CAPTCHA solving, and many other hurdles. As such, companies can provide access to solutions to allow researchers to avoid difficulties.
However, the creation of web scrapers would not be essential if the solutions did not play an important role in the freedom of research. With all the other cases I have described above (outside of manual collection), there is always the risk of bias and publication problems. Furthermore, researchers are always limited by one or other factors, such as the volume or selection of data.
With web scraping, however, none of these problems occur. Researchers are free to acquire the data they need and specialize it according to the study they are conducting. The organizations involved in providing web scraping also have no skin in the game, so there is no reason for bias to appear.
Finally, because there are so many sources available, the doors are wide open to conducting interesting and unique research that would otherwise be impossible. It’s almost like having an infinitely large dataset that can be updated with almost any information at any time.
In the end, web scraping is what will enable academia and researchers to enter a new era of data acquisition. It will not only facilitate the more expensive and complicated research process, but also allow them to break with the conventional problems that come with acquiring data from third parties.
For those in academia who want to get into the future before others, Oxylabs is ready to join forces to help researchers with the pro bono provisions of our web scraping solutions.