A research library built to support discovery.
More than 4,000 peer-reviewed scientific papers accumulated for ItisScientific's research across biotechnology, materials science, biology, chemistry, artificial intelligence, robotics and related disciplines.
Private research archive · local AI knowledge system in developmentAn interdisciplinary scientific archive
ItisScientific maintains an archive of more than 4,000 peer-reviewed research papers assembled across years of scientific investigation and literature research. The papers support knowledge discovery, experimental planning, critical literature assessment and connections between fields.
Biotechnology & biology
Microbial processes, cellular systems, biochemical mechanisms, environmental and biological research.
Chemistry & materials science
Ionic liquids, polymers, chemical transformation, analytical methods, spectroscopy and boron-related chemistry.
Robotics & artificial intelligence
Autonomous systems, data science, machine learning, sensing, perception and intelligent research tools.
Energy & applied research
Renewable fuels, process feasibility, engineering concepts, safety and the translation of scientific literature into experimentally testable questions.
The collection represents a substantial long-term investment in scientific knowledge. Publisher retail prices are not a verified estimate of the archive's current market value or resale value; individual article acquisition rights and licences vary.
Developing a private scientific AI assistant
A long-term aim is to build a locally controlled AI-assisted research environment that helps scientists search references, compare findings and reason over independently prepared scientific notes, original experimental data and material that is permitted for machine use.
1. Structured research records
Bibliographic identifiers, citations, original research notes, factual datasets and summaries are indexed so each finding remains traceable to a source.
2. Evidence-aware retrieval
A retrieval-augmented generation (RAG) system is a potential first stage, enabling a local AI model to find relevant evidence without training its underlying weights on every article PDF.
3. Models trained on eligible material
Private experiments may use original scientific data, independently authored material, open-licensed datasets and publications where the applicable terms permit model training.
4. Scientific validation
Model answers are checked against source records and experimental measurements. Reported results should clearly distinguish observation, inference and hypothesis.
Computing investment and future capacity
Computer and workstation infrastructure has been a multi-year planning and procurement focus. Correspondence in 2023 records acquisition of a 24-core AMD Threadripper Pro processor and an ASUS WRX80 workstation motherboard; memory expansion and a future NVIDIA A100-class accelerator were under consideration. A fully configured 2 TB system or A100 installation is not claimed as established by these records.
Future development will favour private storage, reproducible scientific workflows and a clear rights register for research sources.
Responsible publication use: Access to a copyrighted article, including a purchased copy, does not automatically include permission to redistribute it or use it for model training. ItisScientific intends to use original factual summaries and datasets, licensed content, and tools that respect publisher terms and applicable Australian copyright requirements. The original PDFs are not offered for public download.