Accelerate research, brainstorm and discovery.
Nifty.works is Google map for knowledge landscapes: find connections between topics.
Search for one topic to explore its context. Search for multiple topics to explore connections between them.
What you can doSketch in seconds a whole research for brainstorming and knowledge discovery.
Edit connections to represent a subject and share the map on social networks to engage in learning. Share maps with #ChainLetterKnowledge hastag to see where serendipity goes: your friends can expand them!
Nifty.works are made to uncover industrial domains and access knowledge networks.
Knowledge networks, also called knowledge graph, are structures that helps you graps how entities are connected and discover new knowledge. This mindmap is a knowledge network (also called knowledge graph) where each topic maps its wikipedia article. Browsing through wikipedia hyperlinks will also show a visual map of how things are connected, enhancing knowledge discovery.
So far there are 3 domains: factual knowledge, genomics and food.
Factual knolwedgeBrainstorm over encyclopedic knowledge, as in introduction to your personal researches - a difference respect to Google Knowledge graph is that you can search for paths connecting multiple entities.
GenomicsDiscover how genes interacts with each other, and how genes expressions are associated to diseases. This knowledge networks exposes data from Target-Validation research institute.
FoodDiscover how to eat more sustainably. Search for a recipe you know or for the ingredients you have, and discover new recipes and also which are more sustianable (here, a first prototype of sustainability index reflects carboon footpring of a supply chain associated to an ingredient, currently based on sampling research in Italy.)
Do you want it, too?
Do you want to map your own data into knowledge networks?
Do you want to apply knowledge visualisation to your own data?
Would you like to hire me for advisory/design thinking over exponential tech and design thinking ?
All duable. Please contact me with a short description.
Do you want to support and extend this project?
Do you want to support for uncovering whole industrial domains as public accessible networks ?
Do you want to support my interest for continous learning over exponential tech, its opportunities and governance?
Do you want to support social impact initiatives based on linking people diversity and diverse cultural background ?
Do you want to collaborate and showcase your data-science insights embedded in knowledge discovery tools ?
It all sounds magic! Have a look on rewards and perks on Patreon!
Tell me moreMy interest in Factual Knowledge discovery project is a first attempt to automate knowledge discovery to get directions tailored to your background.
Make sense of any subject you want or need to learn about - no matter of your background. If you query the 5 million topics in this discovery engine by group of 5, you have 2.6 10^31 possible sub-graphs: quite a lot.Next thing is autonomous learning to provide insights about what you aim to know respect to what you knew already: get a personal introduction to the subjects you want to explore. Tecky people also call it "actualise new knowledge".
I would like to know more about unsupervised learning; crowd-sourcing, patrons or funding opportunities about edutech; new governance models for tech innovation aiming, at the same time, to exponential growth and while decentralizing property rights over network infrastructures enabling growth.
A collective memoryThis knowledge graph is a sort of collective memory. I see a collective memory as a structure that can evolve to spur on understanding how do we (creatively) produce new knowledge, knowledge that organize information and experience.
A next step will be a Q/A companion built upon a collective memory, so to uncover "premium" knowledge structures you struggle get into, and to accompany you based on your personal background.At this stage, you can visualise knowledge graphs. Some examples: