Research Data Extraction Services

What is Data Extraction in Research?

Data extraction is the process by which researchers obtain the necessary information from raw data. Data extraction in research has varied requirements. Accordingly, the extraction forms are designed to extract data for data mining and other advanced-level AI-enabled applications. The systematic review process is followed to design a high-quality process for different research purposes. In this context, data analysis and choosing the right extraction method an important role.

Data extraction in research is a necessary step for data mining. The process combines information in tabular form that is further used in processes like data mining in healthcare research, data mining in data sciences, and so on.

One of the important steps toward extracting useful data is to hide the information and present the data sets with different views.

The main purpose of extracting data is to create a centralized database that has precise information. This database can have different data sets providing different views of data for further data mining.

Why is data extraction important?

Data Extraction sets the pipeline for the further extraction process. It is a time-consuming activity followed by deep analysis and research.

The data extraction process requires various considerations such as the sources of data, the method of extraction, and the reliability of the data extracted through this routine.

The accuracy and reliability are the two most important factors in maintaining the authenticity of the extracted data. Professional data management agencies have experienced data analysts to prepare data for useful extraction. Normally, following predefined steps help in a data extraction process.

Textual descriptions of studies
Groupings and clusters
Tabulation
Transforming data into a common view
Using descriptive tools to remove unwanted information
Translating data with thematic analysis
Content analysis by mining the data

What are data extraction methods?

Expert-level data scientists have different ways of extracting research data as it has complex structures. The success of extraction depends on two factors:

The method of extraction -

How to clean and transform the data for further processing?

For extraction, there are two techniques - Logical and Physical.

Logical Extraction further has two options - Full Extraction and Incremental Extraction.

Logical Full Extraction - All data is extracted directly from the source system at once. There’s no need for additional logical/technological information. The research data having simple text is extracted using this method.

Incremental Extraction - The extraction is done in two steps. First, the data is prepared and then extracted. The research or mathematical data having complex information is extracted using this extraction method. In most of the research work, this format is used.

Physical Extraction - Source systems often have certain restrictions or limitations. In this method, unstructured data is recreated by changing its schema and views. Further, two types of physical extraction are done depending on the nature of the data - Online and Offline Extraction.

Online Extraction - The online data extraction process involves direct data transference from the source system to the data warehouse. Extraction tools directly connect with source data or transitional systems to extract meaningful information from it. The process is used by mining the data further to create a structured format.

Offline Extraction - Research data is complex. Sometimes, it is not possible to extract data in one step. For such data, an offline extraction method is used. First, the tools prepare the data, and then extraction is done. Sometimes, data is combined from more than one source.

What is data extraction in a systematic review?

The systematic review represents the evidence tables. Each table has the details of the research carried out and a summary of the facts collected. These tables will help you determine and prepare data for quantitative synthesis. A systematic review contains the following:

Information about the study (author(s), year of publication, title, DOI)

Demographics of data

Methodology to read data

The intervention of data using time/date formats

Outcomes (quantitative and/or qualitative)

What are the tips to do meaningful data extraction?

Once the systematic review of the research data is created, we need to create a schema design with appropriate field names. For example, when data mining in healthcare research papers is done, it divides the data like financial analysis, patient records, patients with chronic diseases, and so on. Each view represents a different aspect of the same data.

Here are a few tips to create an effective systematic review:

Learning the standard forms in which data can be extracted for further use.

Developing the skills to use the AI and ML tools\

Creation of extraction/coding forms to make sure the best data extraction.

Fiding an optimal solution with less time consumption

Documentation of the process followed in extraction to implement changes, if any are required.

How to Choose the best agency for data extraction?

Data extraction is the need of enterprises as we apply data mining processes in mastering the data. When we search for data extraction in research services, there are many agencies on the list. To find the best data extraction company, you should consider:

Mine More and More Information on the Internet

Depending on the need, look for an agency that can provide a complete research data management solution.

Check previous work and client reviews

At eRecordsUSA, we empower your research data extraction with reliable, accurate, and actionable data that is ready for data mining. We also provide technical consulting to transform research data that is in scientific languages. We have provided services to every type of complex research data using advanced mining techniques and tools. Reach out to us for your next data extraction project for a budget-friendly, time-framed, and expert-level solution.

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