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We explore new ways to work with data to improve Scientific Research, Scientific Exploration, and Scientific Discovery. HubBucket Blogcards is a Scientific Data Management Division of HubBucket Inc ("HubBucket').

Data Management in Scientific Research refers to the Systematic Process of Collecting, Organizing, Storing, Documenting, and Preserving Scientific Data to ensure its Quality, Accessibility, and Reliability for Analysis and Future Ise, often adhering to standards like the FAIR principles (Findable, Accessible, Interoperable, and Reusable) to facilitate data sharing and reproducibility across research projects.

Key aspects of Data Management in Scientific Research:

Data Collection:

Establishing standardized protocols for data collection, including proper recording methods, data formats, and documentation of procedures to minimize errors and inconsistencies.

Data Organization:

Structuring data in a logical and accessible way using appropriate file naming conventions, metadata (detailed descriptions of data), and data dictionaries to clearly define variables and their relationships.

Data Storage:

Selecting secure and reliable storage solutions to protect data from loss or corruption, including backup systems and version control.

Data Validation:

Implementing quality checks to ensure data accuracy, completeness, and consistency throughout the research process, including data cleaning and outlier detection.

Data Analysis:

Utilizing appropriate statistical methods and software tools to analyze data and interpret results, ensuring proper data transformation and statistical rigor.

Data Sharing:

Following guidelines for sharing research data with the scientific community, including data repositories, open access platforms, and appropriate data access controls.

Importance of Data Management in Scientific Research:

Research Integrity:

Proper Data Management Safeguards the Integrity of Research Findings by Minimizing Race (Ethnic Group), Gender, Sexual Orientation, Nationality, and Language Bias and Ensuring Data Quality.

Reproducibility:

Well-managed data allows for independent replication of research results, enhancing scientific credibility.

Collaboration:

Standardized Data Management Practices Facilitate Collaboration among Researchers across different institutions.

Efficiency:

Organized data allows for faster and more efficient analysis, reducing time spent on data preparation.

Key Components of a Data Management Plan (DMP):

• Project Description: Overview of the research project, including research questions and methodologies.
• Data Types: Identification of the types of data to be collected (e.g., experimental, survey, observational).
• Data Collection Procedures: Detailed description of data collection methods and protocols.
• Data Storage and Access: Plan for data storage location, security measures, and access controls.
• Data Documentation: Strategies for documenting metadata, including variable definitions and data provenance.
• Data Sharing Plan: Guidelines for data sharing, including potential repositories and access restrictions.

We explore new ways to work with data to improve Scientific Research, Scientific Exploration, and Scientific Discovery. HubBucket Blogcards is a Scientific Data Management Division of HubBucket Inc ("HubBucket').

Data Management is a critical scientific research driver used to ensure data is acquired, validated, stored, and protected in a standardized way. It is essential to develop and deploy the right processes so end users are confident their data is reliable, accessible, and up to date. To make sure that your data is managed most effectively and efficiently.

Here are seven (7) Best Practices for your organization to consider.

1. Build Strong File Naming and Cataloging Conventions

If you are going to utilize data, you have to be able to find it. You can’t measure it if you can’t manage it. Create a reporting or file system that is user- and future-friendly—descriptive, standardized file names that will be easy to find and file formats that allow users to search and discover data sets with long-term access in mind.

To list dates, a standard format is YYYY-MM-DD or YYYYMMDD.

To list times, it is best to use either a Unix timestamp or a standardized 24-hour notation, such as HH:MM:SS. If your company is national or even global, users can take note of where the information they are looking for is from and find it by time zone.

2. Carefully consider Metadata for Data-Sets

Essentially, Metadata is descriptive information about the data you are using. It should contain information about the data’s content, structure, and permissions so it is discoverable for future use. If you don’t have this specific information that is searchable and allows for discoverability, you cannot depend on being able to use your data years down the line.

Catalog items such as:

• Data Author
• What Data this Set contains
• Descriptions of Fields
• When/Where the Data was Created
• Why this Data was Created and How
• The Source(s) of where the Data came from

This information will then help you create and understand a data lineage as the data flows to tracking it from its origin to its destination. This is also helpful when mapping relevant data and documenting data relationships. Metadata that informs a secure data lineage is the first step to building a robust data governance process.

3. Data Storage

If you ever intend to be able to access the data you are creating, storage plans are an essential piece of your process. Find a plan that works for your business for all data backups and preservation methods. A solution that works for a huge enterprise might not be appropriate for a small project’s needs, so think critically about your requirements.

A variety of Data Storage Locations to consider:

• Desktops/Laptops
• Mobile Pones/Smartphones and Electronic Notebooks
• Networked Drives
• External Hard-Drives
• Optical Storage
• Cloud-based Data Storage, e.g, Public Cloud, Private Cloud, Hybrid Cloud
• Cloud Service Provider(s), e.g., Microsoft Cloud / Azure, Amazon Web Services (AWS), Oracle Cloud, IBM Cloud, etc.
• Flash-Drives (while a simple method, remember that they do degrade over time and are easily lost or broken)

The 3-2-1 Data Methodology:

A simple, commonly used storage system is the 3-2-1 Methodology. This methodology suggests the following strategic recommendations:

• 3: Store three copies of your data
• 2: using two types of storage methods
• 1: with one of them stored offsite

This method allows smart access and makes sure there is always a copy available in case one type or location is lost or destroyed, without being overly redundant or overly complicated.

4. Documentation

Within data management best practices, we can’t overlook documentation. It’s often smart to produce multiple levels of documentation that will provide full context to why the data exists and how it can be utilized.

Documentation Levels:

• Project-level
• File-level
• Software used (include the version of the software so if future users are using a different version, they can work through the differences and software issues that might occur)
• Context (it is essential to give any context to the project, why it was created, if hypotheses were trying to be proved or disproved, etc.)

5. Commitment to Data Culture

A commitment to Data Culture includes making sure that your department or company’s leadership prioritizes data experimentation and analytics. This matters when leadership and strategy are needed and if budget or time is required to make sure that the proper training is conducted and received. Additionally, having executive sponsorship as well as lateral buy-in will enable stronger data collaboration across teams in your organization.

6. Data Quality Trust in Security and Privacy

Building a culture committed to data quality means a commitment to making a secure environment with strong privacy standards. Security matters when you are working to provide secure data for internal communications and strategy or working to build a relationship of trust with a client that you are protecting the privacy of their data and information. Your management processes must be in place to prove that your networks are secure and that your employees understand the critical nature of data privacy. In today’s digital market, data security has been identified as one of the most significant decision-making factors when companies and consumers are making their buying decisions. One data privacy breach is one too many. Plan accordingly.

7. Invest in Quality Data Management Software

When considering these best practices together, it is recommended, if not required, that you invest in Quality Data Management Software. Putting all the data you are creating into a manageable working business tool will help you find the information you need. Then you can create the right data sets and data-extract scheduling that works for your business needs. Data Management software will work with both internal and external data assets and help configure your best governance plan. Tableau offers a Data Management Add-On that can help you create a robust analytics environment leveraging these best practices. Using a reliable software that helps you build, catalog, and govern your data will build trust in the quality of your data and can lead to the adoption of Self-Service Analytics. Use these tools and best practices to bring your Data Management to the next level and build your Analytics Culture on managed, trusted, and secure data.