| In Opasnet many pages being worked on and are in different classes of progression. Thus the information on those pages should be regarded with consideration. The progression class of this page has been assessed:
|| The content and quality of this page is/was being curated by the project that produced the page.
The quality was last checked: 2016-04-09.
|Moderator:Jouni (see all)|
|This page is a stub. You may improve it into a full page.|
Knowledge crystals are current best answers to specific research questions. They are produced and distributed openly using crowdsourcing and scientific criticism. For a presentations about their use, see Online collaborative models. Knowledge crystals are extensively used in Opasnet, where they are mainly in forms of variables, assessments, and methods. For descriptions of recent use of knowledge crystals, see Portal:Variables.
What do knowledge crystals have to be like to
- be useful information odjects in impact assessments as they are,
- contain the answer as open data,
- withstand scientific critique,
- be able to measure the use and usefulness of the knowledge they contain,
- be able to, in an acceptable way, hand out scientific merit to the people involved in producing the content?
Knowledge crystals are the basic elements of for example assessments. They always describe a phenomenon of the real world. These can be the descriptions of physical phenomena, like exposure to a chemical, but also for example the population's opinion distribution on immigration. It is in the nature of knowledge crystals they are not final, but their content develops with new information and work put into them. Knowledge crystals are also not tied to any specific assessment, but can be used as parts of multiple assessments. An exception are assessments, that are produced to help with a certain decision, and whose answer doesn't change after the assessment is finished (even though the variables in the assessment may change). Knowledge crystals are also called variables because that's the role they have in assessment models. However, the word variable has so many other meanings that we prefer knowledge crystals in this context.
Another basic feature of a knowledge crystal is its standardised structure that enables the building of assessment models or different internet applications basing on it. So even though the content is updated as knowledge increases, a knowledge crystal remains in the same, computer-readable format. Usually only raw data is in more or less standard format, while the information object containing interpretations from the data are almost without exception made for humans instead of computers, like articles or reports. This makes the knowledge crystal a rare kind of information object: it is computer-readable interpretation of some specific topic.
There are different kind of knowledge crystals for different uses, and they are more accurately described on for example the pages variable, assessment and method. Here is a short description of the most important qualities of a knowledge crystal.
- Knowledge crystals answer a specific research question.
- The answer of a knowledge crystal is the current best synthesis of all available data. Typically it has a descriptive easy-to-read summary and a detailed quantitative result published as open data. An answer may contain several competing hypotheses, if they hold against scientific criticism. This means it also includes an accurate description of the uncertainty of the answer.
- The rationale of knowledge crystals includes all information that is required to convince a critical rational observer of the validity of the answer.
- The content of knowledge crystals is produced by crowdsourcing. Anyone can participate.
- Knowledge crystals are aiming to find shared understanding. It is a situation, where all participants' views have been described well enough so that people can know fact facts and opinions exist about the topic and what agreements and disagreements exist and why.
Different information objects and their usage
Knowledge crystals contain scientific knowledge, but they differ from classic products of scientific research. Here is a short description and comparison.
A scientific article is the basic unit of publishing science today. For it a researcher or a research group produces data, i.e. observations about the world. The data is analysed, and in the end interpretations and conclusions are made based on the new results and previous scientific articles. The goal is to publish the article in a peer reviewed journal. Peer review means that a few researches in the field look through the manuscript and back it up before it is published. The peer review system aims to raise the quality of the manuscripts and weed out bad research. It is commonly agreed that the system isn't especially efficient for either purpose, but no one has come up with anything better. Someone has said that the primary product should be the original data, not an article: researchers should publish what they found, instead of writing descriptions about what they think they found.
Expert reports are gathered by an expert well familiar with the field in question, and are usually about some specific question like the topic of a future decision. They produce new knowledge but not new data. They are usually not peer reviewed, so they're not well respected among researchers and research funders. However, they are much better suited for decision support, because they answer the actual questions that are relevant to the decision at hand.
Open data is usually measured raw data that has been made public for anyone to use. It depends on the case whether the data is well cultured and quality-proofed, but it often has quality issues such as poor meta data. The practises of open data have only begun to take shape in the last few years, because researches haven't been in the habit of publishing raw data before. The problem with supporting decision-making with raw data is that it doesn't involve any interpretations or conclusions, and even less so of the relevant issues. Open data is great raw material for someone who knows how to analyse and interpret it and has the time, but quite useless to anyone else.
The idea of a knowledge crystal is to combine the parts of other information products useful to decision support and avoid the bad parts. The idea of a knowledge crystal is to build an information object around a specific research question. The question can be purely scientific, but in the case of decision support it is usually phrased to help precisely the future decision. To answer the question experts gather all possible material that will help answer the question. This includes research articles, expert reports, open data and all other silent knowledge of the experts that is not found in written form.
The knowledge crystal is worked on from the beginning in an open web-workspace with the help of crowdsourcing, and all information it contains is free to use. The material is structured, assessed and interpreted. The result is an answer that has passed all critique that has come up during the working process. Thus the answer is the best current interpretation of how the thing the question asks is in reality. Criticising the knowledge crystals openly during the work ensures that the answer is scientifically sound. The answer is usually in a computer-readable format for models to use and also in text and picture format for humans.
The strengths of a knowledge crystal are that it uses all relevant information (not only own data as in an article), interprets the data (unlike open data) and is produced by following the principles of openness and critique (unlike an expert report).
Main article: Shared understanding
A key objective of strategic research is to support societal decision making. This should be done already from the beginning by utilising a method called open policy practice. It was developed in THL in 2013 and it is based on long-term experience on decision support in environmental health.   The most important principle of open policy practice is to develop shared understanding about a policy issue at hand. Shared understanding is a situation, where all participants have collaboratively described in writing what is known about the details of the issue, what are objectives of different stakeholders, where there are agreements and where there are disagreements and why. Participation is open and includes decision makers, experts, citizens, and other interested parties.
Shared understanding is reached by utilising systematic methods of collaborative work and participation. When there is disagreement about facts, resolution is found by using criticism and observations - the building blocks of science. The work is supported by modern internet tools such as open data bases, real-time collaborative editing software, wikis, and online computational models. These have been in active use in THL for years, and there is good expertise in such work.
In practice, each research question will have an own internet page on a collaborative web-workspace since the first day of the work. The answer to each question is iteratively built based on existing and new data, analyses, and discussions during the project. Anyone can participate in these discussions at any time, and the team members will moderate the discussions. The answers are updated regularly as new information arises, and the current best answer is available for users as open linked data at any given time. Web pages that are built in this way around relevant research questions are called knowledge crystals. 
It is important to notice, that some of the research questions are designed in a way that they offer practical and direct guidance to relevant and timely policy issues. Knowledge crystal work should actively seek collaboration and contributions from policy makers to develop relevant questions and to include policy perspective to the work. Knowledge crystals are a practical solution to the collaboration need on science-policy interface. This work is supported by more traditional methods of communication and collaboration, such as reports, policy briefs, stakeholder workshops, and press releases.
- Tuomisto, Jouni T.; Pohjola, Mikko; Pohjola, Pasi. Avoin päätöksentekokäytäntö voisi parantaa tiedon hyödyntämistä. [Open policy practice could improve knowledge use.] Yhteiskuntapolitiikka 1/2014, 66-75. http://urn.fi/URN:NBN:fi-fe2014031821621
- Pohjola MV, Leino O, Kollanus V, Tuomisto JT, Gunnlaugsdóttir H, Holm F, Kalogeras N, Luteijn JM, Magnússon SH, Odekerken G, Tijhuis MJ, Ueland O, White BC, Verhagen H. State of the art in benefit-risk analysis: Environmental health. Food Chem Toxicol. (2012) 50: 1: 40-55. 
- Tuomisto JT. Massadata kansanterveyden edistämisessä. [Big data in promotion of public health.] Duodecim 2015;131:2179–87. URN:NBN:fi-fe201601071478
The term knowledge crystal has been used independently in several places in the popular culture. In all cases, it seems to describe a concrete object that has useful information and knowledge in a very condensed form. The term has been used by e.g. Superman, heroes in Middle Earth, Bionicles, and Guild Wars 2