Research Database Management (RDM)

Frequently Asked Questions

At TAGDit we believe that data management is fundamental for the growth of companies. For that reason, we want to rethink research data management (RDM) from a corporate point of view. Below, we have created a bank of Frequently Asked Questions that may resolve the doubts of some of our users. We hope that each of these questions can provide accurate and quality information that will contribute to the growth of your company.

In the corporate world, RDM can be interpreted from two points of view. On the one hand, we can translate the acronym as Rational Decision Making and, on the other, as Risk Decision Making. In the following, we will see how it works in each case.

In the first case, we refer to the ability of companies to make decisions rationally, i.e. based on a logical system. This means that there is a team willing to collaborate in the planning and evaluation of the available data management options, as well as in the evaluation of their risks and benefits at the corporate level. The purpose of this process assumes that it is possible to select the best option according to the company’s goals and objectives. In this sense, Rational Decision Making means implementing a system that includes the identification of a problem, the generation of alternatives, the evaluation of these alternatives and the selection of the best option.

In the second case, on the other hand, we refer to the process by which companies have the ability to assess and manage the risks associated with the decisions they expect to make. This involves identifying and understanding how such risks may affect corporate interests, in such a way that it is possible to calculate the probability of their occurrence, as well as to determine strategies that help mitigate such risks in the most efficient way possible. The objective of Risk Decision Making is to anticipate and respond proactively to the challenges and opportunities that a company faces in relation to its decisions and, consequently, to the management and use of its data. This is the only way to make informed decisions that maximize the value of data while minimizing exposure to unwanted risks.

Taking these two points of view into account, we believe that it is possible to create a synthetic version based on the academic notion of Research Data Management (RDM). In that sense, our approach suggests the importance of resorting to an academic model that allows you to rationally plan how to manage your data before you start any corporate project. In this way, your team will be able to identify the problem, evaluate alternatives and make accurate decisions about data management before, during and after the project is completed. This will help minimize risks and establish criteria about what data you can make available to others, when to make it available, and how to do it properly.

In general terms, we can identify the following formats as the most appropriate for creating a data file in accordance with Research Data Management:

  • Textual data
  • Images (born-digital or digitized, moving or still)
  • Audio Files
  • Numerical tables
  • Survey or questionnaire responses
  • Interviews (recordings, transcripts)
  • Geospatioal information
  • Content or thematic analyses
  • Artists’ notes
  • Genomic information

Apart from the fact that RDM is an excellent strategy to establish a decision system based on the study and rational evaluation of data, as well as to mitigate the risks of decisions made at the enterprise level, there are some special benefits worth mentioning:

  • The implementation of RDM in your company makes the research process more efficient every day. That is, it facilitates data management and establishes a system that allows you to regulate the research process, so that there is a selection filter according to the company’s objectives and goals.
  • Given the selection criteria, this system can help you meet your company’s ethical and legal requirements, as well as funding and publication objectives.
  • Similarly, it can help make your research reproducible in any format, as well as enable you to demonstrate that you have done it responsibly.
  • Implementing RDM will also allow you to increase the impact of your research and reduce your research costs by having a planning roadmap to help you throughout the process.
  • In the end, it can help facilitate professional, scientific and business research in a transparent and effective manner.

Often, data are used as primary sources that support some type of technical, scientific, academic or, in this case, corporate information. These data systems are accepted by a certain community due to the validity they represent in the research environment. However, such research does not always speak of the same thing, i.e., it varies depending on the interests and objectives of the company. For this reason, managing information sources can become a complex and difficult exercise.

To solve this problem, it is necessary to create a storage system that helps us to classify in a database all the information collected. To do this, it is necessary to establish a mechanism that allows us to select and classify the data in such a way that, at the time of performing a search, it is possible to access the information independently, which makes the work faster and more effective. This, of course, depends on the user’s search intention (whether thematic, utility, etc.). But the goal is that the user knows where to look for such information. Therefore, it is important that they know what Metadata are.

Metadata is a type of data that allows you to organize other data representing primary search sources. In other words, it is a schematic search source that allows you to access the management and governance of a company’s data. In this sense, three types of Metadata can be established.

  • Descriptive: those that describe the context and allow you to discriminate the content of the datasets through, for example, the title, the author’s name or keywords.
  • Administrative: those whose information is necessary to use the data and the dataset, which are managed, for example, through software requirements, licensing, etc.
  • Structural: those that show, finally, how the different datasets are related to each other and the processing/formatting steps that have been taken in the search engines through the two previous types of Metadata. In other words, it is the one in charge of compiling and offering the most appropriate options to the search intention according to the descriptive and administrative information.

It should be noted that Metadata allows your company to create an interdisciplinary search outline, which means that information sources can offer a set of options that provide different perspectives around your search intent. But this does not mean that your information will not be presented in a sorted and classified manner. Rather, because of this, different companies (as well as different disciplines in academia) use metadata standards and practices differently and independently.

Tri-Agency is a research funding model involving three Canadian government agencies. These agencies are:

  • The Natural Sciences and Engineering Research Council, which is responsible for funding projects in those areas of knowledge.
  • The Social Sciences and Humanities Research Council, which is responsible for funding projects related to these areas.
  • The Health Sciences Research Council, which is responsible for financing projects related to this area of knowledge.

These three councils establish the principles and standards for responsible conduct in research funded by the Canadian federal government. When we speak of responsible conduct, we refer to the set of policies that must be implemented within the research project, so that its development not only complies with the standards established in terms of quality, but also guarantees the ethical, legal and transparent use of the databases. Thus, the Tri-Agency makes possible the coordination and collaboration among the various research groups in order to regulate and distribute in an equitable manner the funds destined to the development of their projects.

Our goal is to make this model an important reference point in the management and distribution of funds within the Research Data Management (RDM) system. In this sense, we understand the Tri-Agency, precisely, as the set of regulations within the research process and data management that allow validating those data as primary sources to support technical, academic, corporate and scientific research. In turn, this model is used as a test council whose objective is to ensure compliance with the policies in the research process of a company, so that it is possible to validate its findings and results from the point of view of the legal and corporate community.

Some of the Tri-Agency’s policies can be found at the following link: Tri-Agency Research Data Management Policy (science.gc.ca)

This term generally refers to adding tags to a website in order to organize and categorize certain content, whether it is technological, scientific or corporate research content. In the context of a code repository, for example, this may refer to tagging specific versions of the website’s source code in order to effectively track versions and manage changes.

In this sense, taggit website means creating a data selection history that allows organizing information around a corporate-level research project, whose management and governance center is in the hands of a Metadata system that is, in turn, managed in a geospatial way, i.e. it implies a synchronization between the place and time of data search. Thanks to this, it is possible to have a record that allows progress to be made on a given project and to share or facilitate collaboration with other research projects.