Exploring the evolution of interdisciplinary research, from Open Access to AI integration and beyond
Imagine a world where humanities scholars regularly collaborate with data scientists, where ancient historical methods combine with cutting-edge AI technology, and where research breaks free from traditional academic silos to tackle society's most pressing problems. This isn't a vision of the future—it's the story of the past decade in interdisciplinary research.
Ten years ago, a bold experiment began with the launch of a journal dedicated to bridging the vast divides between academic disciplines. What started as Palgrave Communications in 2015 has evolved into Humanities and Social Sciences Communications, growing from a niche publication into a leading international forum for robust, peer-reviewed scholarship that crosses traditional boundaries 1 .
This decade has witnessed profound transformations in how research is conducted, shared, and applied to real-world problems. From the rise of open science to the explosive emergence of artificial intelligence, the research landscape has been reshaped by forces both technological and cultural. As we examine the key developments of the past ten years through the work of our authors, we'll explore not just what has changed, but how these changes are creating more inclusive, transparent, and impactful research practices across all fields of human knowledge 1 .
Launch of interdisciplinary journal with focus on bridging academic divides
Open Access gains significant traction in humanities and social sciences
FAIR data principles become more widely adopted across disciplines
Generative AI emerges as transformative tool following ChatGPT launch
Agentic AI begins to reshape research methodologies and workflows
A decade ago, Open Access (OA) publishing was still a novel concept in many humanities and social sciences circles. Today, it has become an increasingly central part of the research ecosystem. The journal that began this journey in 2015 was "born Open Access," championing a model that brings numerous benefits to all who rely on the outputs of humanities and social sciences research 1 .
The FAIR data principles—emphasizing that research data should be Findable, Accessible, Interoperable, and Reusable—have gained significant traction. While practices vary between fields, there's growing recognition that transparent data practices enable better verification of results and more efficient use of research resources 1 .
Studies indicate that the lifetime prevalence of open science practices among humanities and social sciences researchers has surged from 49% in 2010 to 87% a decade later 1 .
| Research Aspect | Ten Years Ago | Current State | Impact on Research |
|---|---|---|---|
| Access Models | Traditional subscription-based; OA awareness "patchy" in HSS | Growing OA uptake; funder mandates (e.g., Plan S); increasing HSS support | Research more accessible beyond academia; faster dissemination |
| Data Sharing | Limited in HSS; unclear protocols | Movement toward FAIR principles; data availability statements encouraged | Improved research transparency; enables verification and reuse |
| Interdisciplinarity | Traditional boundaries firmly entrenched | "Fast-dissolving" boundaries; increased collaboration across fields | More holistic approaches to complex problems; methodological cross-pollination |
| Technology Integration | AI in early stages; limited researcher use | Widespread AI experimentation; tools for writing, analysis, and review | Accelerated research processes; new methodological possibilities |
The past decade, particularly the last few years, has witnessed AI technologies evolve from specialized tools to transformative forces across research disciplines. Since ChatGPT's launch in November 2022, generative AI has become increasingly integrated into mainstream applications, with leading companies like Salesforce and Adobe embedding it into their platforms 3 .
In research contexts, AI offers enormous potential when managed carefully with human oversight. More researchers are experimenting with AI tools for tasks ranging from data gathering and methodology design to manuscript writing 1 .
Perhaps most significantly, we're now seeing the emergence of Agentic AI—intelligent systems that can think, plan, and act autonomously. Unlike traditional chatbots, these AI agents promise to solve complex problems by creating multi-level plans, learning from data, and making decisions based on this learning 3 .
"This is what AI was meant to be... I think this is going to change companies forever. I think it's going to change software forever" 3 .
To understand how research practices have evolved, let's examine a specific experiment that exemplifies the precision and transparency of modern scientific inquiry. This experiment focuses on tracking the movement of a key transcription factor (NF-κB p65) in HeLa cells in response to inflammatory signals, using advanced imaging techniques that have become more accessible and sophisticated over the past decade.
The experimental process was carefully designed to capture cellular changes with high precision:
The experimental results demonstrated a dramatic translocation of NF-κB p65 from the cytoplasm to the nucleus following inflammatory stimulation—a shift that was both quantitatively substantial and statistically significant 5 . This movement patterns represents a fundamental cellular response mechanism to inflammatory signals.
The prevention of translocation by BAY 11-7082, a specific inhibitor of NF-κB activation, confirms the specificity of this response and highlights potential therapeutic strategies for controlling inflammation 5 . This experiment exemplifies how precise molecular tools combined with rigorous methodology can reveal fundamental biological processes with important implications for understanding inflammation-related diseases.
What makes this experiment particularly reflective of modern research practices is its transparent methodology and potential for data sharing—hallmarks of the open science movement that has gained momentum over the past decade. The detailed documentation of reagents, concentrations, and procedures enables other researchers to reproduce and build upon these findings 5 .
| Experimental Condition | Nuclear Cytoplasmic Ratio (Mean ± SD) | Percentage of Cells Showing Significant Nuclear Localization | P-value (vs. Control) |
|---|---|---|---|
| Control (Untreated) | 0.45 ± 0.12 | 8.2% | - |
| TNF-α Treatment (10 ng/mL) | 2.86 ± 0.45 | 89.5% | < 0.001 |
| IL-1α Treatment (10 ng/mL) | 2.94 ± 0.51 | 91.2% | < 0.001 |
| BAY 11-7082 Inhibitor + TNF-α | 0.62 ± 0.18 | 12.8% | < 0.001 |
Modern research relies on specialized reagents and materials that enable precise investigation of biological processes. The table below highlights key reagents used in cellular research, illustrating the sophisticated tools available to today's scientists.
| Reagent/Material | Function in Research | Example Application |
|---|---|---|
| Formaldehyde | Fixation agent that preserves cellular structure | Maintaining cell architecture for accurate imaging 5 |
| Triton X-100 | Detergent that permeabilizes cell membranes | Allowing antibodies to access intracellular targets 5 |
| Primary Antibodies | Bind specifically to target proteins | Identifying localization of specific proteins like NF-κB 5 |
| Fluorescent Secondary Antibodies | Detect primary antibodies with visual signals | Enabling visualization of protein location 5 |
| Hoechst 33342 | DNA-binding fluorescent dye | Identifying nuclear location in cells 5 |
| Recombinant Cytokines | Signaling proteins that stimulate cellular responses | Inducing inflammatory responses in experimental models 5 |
| BAY 11-7082 | Specific inhibitor of NF-κB activation | Blocking inflammatory signaling to study pathway mechanisms 5 |
| Cell Culture Media | Nutrient-rich solutions supporting cell growth | Maintaining cells in controlled laboratory conditions 5 |
The sophistication of research tools has increased dramatically over the past decade. Modern reagents offer greater specificity, lower toxicity, and enhanced compatibility with advanced imaging and analysis techniques.
This evolution enables researchers to ask more precise questions and obtain more reliable answers, accelerating the pace of discovery across biological and medical sciences.
As we reflect on the transformations of the past decade, it's clear that the evolution of research practices is accelerating rather than slowing. Several key trends are likely to shape the coming years:
As AI systems become more integrated into research processes, the demand for explainable AI (XAI) will grow. Techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) will become increasingly important for making AI decision-making processes transparent and interpretable, especially when AI is used in critical applications like healthcare and public policy 3 .
The same technologies that empower legitimate researchers can also be misused. The past decade has witnessed a proliferation and diversification of scholarly malpractice, requiring more sophisticated approaches to research integrity. Future developments will likely include improved detection systems and greater emphasis on research ethics training 1 .
The complex problems facing society—from climate change to political instability—require joined-up solutions from multiple stakeholders. Humanities and social sciences scholars will play an increasingly important role in interpreting our changing world and contributing to policy responses 1 .
The past decade has demonstrated that breaking down barriers—between disciplines, between researchers and the public, and between traditional and innovative methods—produces richer, more meaningful research. As this evolution continues, the core mission remains unchanged: to support the creation and dissemination of robust knowledge that helps us understand our world and address its most pressing challenges 1 .