Biotech Skills Standards Over 30 Years

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SkillsDB

For nearly 30 years, the biotechnology community has invested enormous effort in defining the skills technicians need to succeed. The result is a rich but unwieldy body of work—hundreds of pages of skill standards, largely locked in PDFs that are difficult to search, compare, or reuse.

Three years ago, Digital World Biology and Biotech-Careers.org set out to improve reuse of these data by building a Skills Database (SkillsDB) for Biotech-Careers.org. The original vision was to create a searchable, rationalized database drawn directly from the skill lists contained in the standards documents. A prototype was developed, and skills from three of the 11 documents were added. However, the extensive manual copying and pasting required to enter the all of data into HTML forms—combined with inconsistent formatting, terminology, and organization across documents—made it clear that the effort required to complete this work would exceed the value it would provide. In addition, while skill standards are essential for educators and credential developers, their generalized language and formal structure are poorly suited for students and others seeking to understand what is required to work in the industry.

Instead, more than 40 years of combined biotechnology teaching and industry experience, along with existing resources, were used to simplify skill descriptions so they more clearly convey what job seekers need to know for different biotech roles. Using this approach, the Biotech-Careers.org skills database was launched in 2024, followed by a similar database on InnovATEBIO.org in 2025. 

Using AI to Analyze the Existing Standards

With the emergence of generative AI (Artificial Intelligence) and large language models in 2022 and 2023, new tools became available for analyzing large, complex document collections. Rather than attempting to manually restructure the standards, these tools offered an opportunity to examine the existing documents more efficiently and to better understand their scope, structure, and evolution. And, we can see if AI can be used to extract the kinds of data as was originally sought.

To explore this, the 11 skill standards documents originally considered for SkillsDB were loaded into Google’s NotebookLM (NLM). Together, the documents total approximately 90 MB. Many are graphics-heavy and designed as large-format brochures rather than data-centered resources. They are not FAIR.* The 1995 Gateway to the Future document, is a scanned PDF with text that is difficult to extract reliably—an issue familiar to anyone who has worked extensively with PDF-based source material.

Document Scope and Scale

An initial question—“How many total pages are in the documents?”—produced a useful estimate. NLM reported that the documents range from short six-page summaries of key activities to a comprehensive 251-page volume for the original Gateway project, with a total exceeding 640 pages. After two documents (biofuels and medical devices) were added, the estimate increased to approximately 689 pages (footnotes).

These figures were inferred from content rather than literal page counts, and one clear overcount was noted.* However, an overall total near 700 pages is reasonable. For example, the Gateway to the Future PDF contains 126 pages, but the original two-column layout has a page number with each column, resulting in 251 pages. This illustrates both the usefulness of AI-assisted estimates and the need for verification and contextual knowledge.

How Many Skills?

The next question addressed the core challenge: how many skills are described across the standards? Here, NLM reinforced the limitations encountered in earlier database efforts. Although the standards generally organize content into critical work functions, key activities (skills), and performance indicators, this framework is applied inconsistently across documents. Common skills are grouped differently and described using varied terminology.

NLM analyzed only three of the 11 documents in detail, identifying 265 skills across those documents. While incomplete, this result confirms the scale of the content and the substantial effort required to fully rationalize it into a single structured dataset. On a positive note, NLM did identify a common skills standard structure (footnotes). 

How Have Skills Changed?

NLM was more effective at identifying patterns in how skill requirements have evolved. Early standards emphasized general laboratory research, reflecting the industry’s origins in recombinant DNA technology and monoclonal antibody development. By 2006, standards expanded to include large-scale fermentation (now often referred to as bioindustrial biotechnology), downstream processing, and preventive maintenance.

Medical device standards emphasized post-market surveillance, risk management (ISO 14971), and design controls. As Cell and Gene Therapy standards emerged, concepts such as scale-out, rather than scale-up, were introduced to address personalized manufacturing and parallelization. Automation, clinical trials data management, and increasing regulatory complexity also became prominent. Across all sectors, digital and informatics skills became substantially more important.

Education Strategy Reflected in the Standards

The extracted data also reflect changes in education strategy. According to NLM, early “stepped” certification models evolved into stackable credential frameworks. These credentials are intended to be portable and recognized across regions, supporting workforce mobility and helping address persistent talent gaps.

Capabilities and Limitations

NotebookLM can summarize content in multiple formats, including text, tables, mind maps, slide decks, and infographics. The infographics included here highlight:

1. The evolution of skill standards (above)
2. Core competencies, including industry validation (below)
3. Industry and other participating advisors (last)

The core competencies graphic incorporates validation data showing how many companies identified each skill as important. The industry graphic demonstrates the scale of participation, with more than 1,000 contributors from over 300 organizations.

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The original goal of extracting structured skill data from all 11 documents remains a work in progress. Global extraction proved difficult, while extraction from individual documents was far more successful. For example, the prompt “Focus on Gateway to the Future—list the 108 skills” produced a well-organized list by job function. A similar prompt focusing on core skills produced key activities aligned with critical work functions and matched the original documents reasonably well. Performance indicators, however, were summarized separately rather than nested under key activities.

These limitations largely reflect the way the standards were created: as presentation-oriented PDFs rather than data designed for reuse.

This analysis shows that biotechnology skill standards represent a substantial, carefully developed body of work, but one that is difficult to reuse in its original form. AI tools such as NotebookLM can rapidly surface document scope, recurring frameworks, and broad patterns in skill evolution, while also exposing long-standing challenges related to inconsistent structure, terminology, and PDF-based formats. While targeted extraction from individual documents is effective, comprehensive rationalization across all standards remains constrained by source design rather than analytical capability. The infographics included here help make this body of work more accessible by revealing what is otherwise buried in hundreds of pages of PDFs—showing how skills have evolved over time, which core competencies are most consistently validated by industry, and the scale of participation that shaped these standards.

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Footnotes

* FAIR refers to digital research data that adheres to principles of being Findable, Accessible, Interoperable, and Reusable (FAIR) to improve data management and facilitate sharing, particularly for machine-driven analysis.

Documents, page number counts, and NLM page count explanation. 

1. 1995-Gateway_to_the_Future.pdf  -- 251 pages
2. 2006-nbc2-biopharmaceutical-manufacturing-industry-skill-standards.pdf -- The Table of Contents lists the final job analysis section on page 47.
3. 2008-biotechnologyskillstandards.pdf -- The Table of Contents lists the final "References" section starting on page 66.
4. 2013-core_lab_skills_5_13_without_checklist.pdf -- Contains at least 30 pages, as indicated by the page numbering in the Appendix.
5. 2013-draft-skill-standards-educating-biofuels-production-analysis-technicians.pdf -- Consists of approximately 24 pages*
6. 2013-nbc2-biomanufacturing-key-activities.pdf -- This summary is at least 6 pages long.
7. 2013-guidedevrecusess.pdf (item 4) -- The Table of Contents lists the final "Appendix B" starting on page 27
8. 2016-Core_Skills_Booklet_low_res.pdf -- Contains at least 30 pages, as indicated by the page numbering in the Appendix.
9. 2016-MedDevCore.pdf (item 5) Consists of approximately 25 pages (missed in first count)
10. 2022-cell-and-gene-therapy-standards.pdf -- The Table of Contents lists "Additional Resources" on page 27
11. National Immunotherapy Labor Market report.pdf -- The Table of Contents indicates that the final section, Appendix E, begins on page 177

*One of the missed documents was a four page spreadsheet document that was counted as 24 pages because each page had a page number ranging between 24 and 28. NLM used the first page number as the count.

NLM Skills Count

Total Number of Skills

The total count of skills depends on the specific "book" or standard being referenced:

• Gateway to the Future (1995) identified 108 tasks, grouped into 34 "Integrated Skill Standards" scenarios.
• NBC2 Biomanufacturing Key Activities (2013):  identified 127 total "Key Activities" (equivalent to competencies or tasks).
• Core Skill Standards for Bioscience Technicians (2016):  identified 30 specific Key Activities nested under six Critical Work Functions.  

The Common Framework

The documents adhere to a common structural framework that places technical tasks within a broader occupational context. This framework is defined by the following hierarchy:

1. Critical Work Functions (CWFs): These represent the broadest areas of responsibility for a technician, such as "Maintaining a safe work environment" or "Performing measurements and tests".
2. Key Activities (KAs): Nested under each CWF, these are the essential tasks a worker performs regularly. They are specific enough to be measured and observed.
3. Performance Indicators: These provide the benchmarks to determine if a Key Activity is performed well.
4. Underlying Technical Knowledge: This includes the expertise (e.g., biology, chemistry, or metrology) required to perform the Key Activity with excellence.
5. Assessments: These are performance-based exercises designed to mimic actual workplace activities to test a student's or worker’s proficiency.

Core Framework Themes

Beyond the structural hierarchy, the framework is built on two guiding concepts established in the 1995 Gateway project:

• The Learning Occupation: Rather than training for a single job title, the framework prepares workers for a "cluster" of related occupations (e.g., research technician, manufacturing operator, and validation technician) that share a common core of skills.
• Integrated Skill Standards: This concept moves away from "checklists" of discrete tasks. Instead, it places tasks and the "know-how" required to perform them into the context of real-life work scenarios that require decision-making and problem-solving.
• Stackable Credentials: The framework is designed to support "portable, industry-recognized credentials" that allow workers to move through career pathways by accumulating specific skill sets.

Finally, the framework integrates Academic and Employability Knowledge and Skills (such as mathematics, communication, and teamwork) directly into the technical standards to ensure workers have the necessary foundation for high-performance workplaces.