The overarching mission of Research Analysis is to accelerate the rate of scientific discovery through the provision of knowledge management and discovery tools to scientists.
Our knowledge management platform:
- captures scientific claims from the literature in a formalised structure.
- rapid and powerful search tools to find existing scientific claims
- analysis tools that assess:
- the level of support for a claim in the literature
- conflicting claims
- knowledge gaps in the literature
- help scientists identify unique and promising hypotheses to test experimentally
- is intuitive and easy to use
We have found that past efforts to build similar tools tend to focus on the computational aspects of knowledge management: How can we get all of the knowledge out of the literature and scientists heads and into a database that can then be mined by the geeks and AIs? This tends to imply that if only the computers could get all of the information, then they would do a better job than the humans. We disagree.
We don’t believe in a Robot Scientist future! We believe that humans continue to have a far superior ability to make the intuitive leaps that lead to scientific discovery. But on the other hand we believe that the computers beat us hands down on being able to manage vast stores of information and to be able to process this information rapidly using logical analysis. We believe that by providing powerful and intuitive knowledge management tools to leading scientists that they will be able to make higher quality intuitive leaps and at a faster rate. The goal of RA is not to extra the knowledge and feed it to the robots, but instead to feed it back to the scientists using powerful tools that make it easy and fast to interrogate.
Some examples of benefits that can come from using Research Analysis include:
- We have all had the experience of remembering a finding from a paper, but not the specific details. Even once you find the paper, were is the specific support in the paper. RA puts these key findings at your fingertips.
- Even the greatest scientists of all time regularly thought they had a new discovery, but later (sometimes much later) found that some obscure scientist had already come up with it 10 years prior. RA allows you to rapidly identify if there is existing support for a scientific claim.
- On the other hand, you want to know whether a scientific claim is valid. Use RA to find all of the papers that support and challenge the claim to assess its merit.
- Use the analysis tools to visualise matrices of scientific claims to identify conflicts, trends or gaps in the literature. A powerful source of new research topics.
Research Analysis is constantly looking for opportunities to improve and extend our platform to support scientists in their work. The platform was originally designed to meet the challenges of our collaborators in their research and we feel that the best way to improve the platform is through helping scientists solve hard problems. We would appreciate feedback, suggestions and the opportunity to work with scientists to help solve their problems.
First find the claim in the View Claims table, then click the View link in the table to go to the claim page. Here you’ll find a unique link to the claim page that makes it easy to share the claim with other users of Research Analysis or the public.
To edit, delete or see more details relating to a claim you can click the View link in the View Claims table and go to the claim page.
You can add claims to the database via two means:
- Add Claim on View Claims page: To enter a claim on this page you simply need to enter the values into each of the boxes at the top of the table and then press the “Add Claim” button. The nice thing about this method is that it simultaneously searches the database to see if the claim already exists and you’ll see related claims as you enter each of the terms.
- Upload a Spreadsheet of Claims: To load a batch of claims you can go to the Upload Claims menu option and select and upload a spreadsheet containing claims. The spreadsheet will need to be in the right format and you can learn more in this article.
Wherever possible, claims should use standardised language for the elements of the claims. Research Analysis currently requires that all terms should follow the Medical Subject Headings (MeSH) standard. Elements of claims should either exist in MeSH as Subject Headings or any of the Entry Terms for a heading. For example, Humans is a Subject Heading in MeSH, but you can also use the Entry Terms which include Human, Homo sapiens, Man. We do allow for new terms to be added where there is no satisfactory MeSH term, however the use of MeSH terms allows for much more powerful search and analysis. For example, a scientist in one field may use a different term to those in another field, but using MeSH terms we can link these two claims together and provide cross-field visibility. In future we also hope to provide searching and analysis across different levels of the MeSH tree eg. Hominids or Mammals would consolidate claims for species further out on the tree.
The Research Analysis platform is centred around the concept of the scientific claim. The goal of a claim is to take a hypothesis that has been argued in the literature and state it in a formalised language that allows for easier comparison, searching and analysis.
While there are several types of scientific claim, Research Analysis is currently focused on Cause-Effect type scientific claims eg. Statins decrease Cholesterol in the Blood of Humans. We have broken up the components of Cause-Effect claims and formalised their language and structure to make them easier to compare, search and analyse. The easiest way to see how they work is to look through some examples in the View Claims table, but here are some details:
- Claim Elements:
- Treatment/Cause: This is the drug, environmental, genetic, etc cause that is made in the model system. eg. Statin treatment
- Effect: This is the type of effect that results from the Treatment/Cause. Currently we only offer three options: increases, decreases or not significant.
- Molecule/Disease: This describes the molecule or disease that is effected by the cause. eg. Cholesterol
- Organ Model: This describes the organ of the animal or the in vitro system where the cause-effect relationship was observed. eg. Blood
- Genetic Model: This describes the genetic model in which the cause-effect relationship was observed. eg. ApoE -/-, Wild Type.
- Species: This describes the species in which the cause-effect relationship was observed. eg. Human.
- Standard Terms: Wherever possible, claims should use standardised language for the elements of the claims. Research Analysis currently requires that all terms should follow the Medical Subject Headings (MeSH) standard. Elements of claims should either exist in MeSH as Subject Headings or any of the Entry Terms for a heading. For example, Humans is a Subject Heading in MeSH, but you can also use the Entry Terms which include Human, Homo sapiens, Man. We do allow for new terms to be added where there is no satisfactory MeSH term, however the use of MeSH terms allows for much more powerful search and analysis. For example, a scientist in one field may use a different term to those in another field, but using MeSH terms we can link these two claims together and provide cross-field visibility. In future we also hope to provide searching and analysis across different levels of the MeSH tree eg. Hominids or Mammals would consolidate claims for species further out on the tree.
- Supporting Quotes: Research Analysis requires that there be a quoted statement taken from the literature that supports each scientific claim in the database. While the whole research paper is required to fully support a claim, the quoted statements provide some context to the claim in the words of the scientist. It also makes it easier for other scientists to identify the section of the paper that was used to create the formalised claim.
- Referencing: Along with the Supporting Quote, Research Analysis requires that the PMID is provided for the paper that the Supporting Quote was taken from. This allows users to quickly dive into the paper for more information or context by clicking on the PMID link in Research Analysis. We also provide the option to provide a detailed citation.
Research Analysis allows researchers to upload claims in bulk using a spreadsheet via the following steps:
- Select the “Upload Claims” menu option.
- Click the “Select files…” button
- Select the spreadsheet in the correct format (refer below) that you wish to upload.
- Check if there were errors with the upload. If there are errors, correct these and reload the file (refer below). You can reload the whole file as duplicates will be ignored.
Formatting the spreadsheet
- The spreadsheet must be in the format required for Research Analysis. The easiest way to do this is to use the template spreadsheet provided via the below link. Don’t change any of the column or tab headings or the upload will fail.
Template Causal Claims – 20150909
Alternatively you can create an example spreadsheet by making a selection of claims on the View Claims page and then Export as Excel. The downloaded Excel file will be in the correct format for upload to Research Analysis.
Correcting Errors in the Spreadsheet
The following provides some descriptions of common errors:
- “Claim [Claim row number] was REJECTED due to a missing [Column Name] value” : The columns Treatment or GENE-KO, Increases Molecule/Disease, In Organ/Cell, Genetic Model, Species, Statement (Quote from paper) and PMID are required fields. If for example there was no Genetic Model specified for a claim then you should put “Not Specified” in this column or if “Wild Type” then enter that value in the column.
- “Sheet: [Sheet Name]. Col: [Column] Incorrect columns found”: This error means that the sheet/tab specified has the incorrect column names. If this is a sheet without claims then this is not a problem and can be ignored. If it does have claims then the columns must be fixed.
If you have a specific question, please comment here and we’ll provide a knowledge base article with a response.