White paper

The most of material world around us is anonymous, faceless, “disconnected” from its creators. Aside of inventors of the world’s prominent products of mass production, there are billions, and ever growing amount of objects of the material world that are designed, built, manufactured by almost everyone on the planet. The most common work outcome in product development and manufacturing is incremental upgrades and continuous development.

Millions of engineers and workers do incremental developments of the products and services in all industries worldwide. If employed, the records of who did what belong to employers in a technical and other kind of business format. Oftentimes information is a subject of confidentiality agreements, and normally such records don’t become a public domain even if not legally protected at some point of time. If self-employed, there could be no records, unless the work outcome is some kind of a recognized piece of art, design, performance, recorded for sale purpose. In general, material world built around us is impersonalized. Thanks to technology developments, in XXI Century millions of people on Earth leave digital fingerprints of their personality dimensions – personal lives, social activities, professional work and its outcome. For centuries to come, historical facts of our modern life have all technical means to be preserved for future generations as long as civilization on Earth exists.

Humankind is about to learn much more about itself by digitizing the entire world around us.

We are a mix of biology, inherited in genes and environmental influences family, social group, education, social class standing in the community. There are some kind of correlations between the individual’s biological “nature” and personal characteristics (traits), preferences, values, religion, insights on life.

“White men cannot jump”, “Italians are good singers”, “African Americans are the best in basketball” – this kind of statements are quite common, but are there scientifically proven and statistically sufficient datasets to qualify how accurate such assumptions can be?

The Problem

Possibilities to establish new correlations between human complete genome sequencing parameters, and individual’s characteristics that can’t be determined by environmental influences (intervention studies), become more affordable. However, obtaining relevant, side-by-side datasets of such parameters for the same massive of research participants is prohibitively expensive, and oftentimes impossible. Those, who provided their bio material for DNA test, may never agree to provide other relevant information about them. It seems unlikely that the ultimate solution may exist. Another problem is that for new, unexpected correlations it is difficult to predict what kind of information may constitute relevant dataset. For example, at the time of planning research, scientists could only guess that the correlation between genome characteristics and educational attainment will be established. In other words, it is difficult to obtain genome characteristics and variety of all kind of could-be-relevant information from statistically sufficient massive of research participants at the time when DNA data obtained. More

Datasets

In the new era where we are moving into, various types of personal data will be collected over the lifetime. Today’s “life data ecosystem” concept (1) is aimed to build a platform to establish an economy for exchanging genetic data and related information. Collecting various types of personal data over the lifetime can yield unprecedented healthcare benefits for the individual and for the economy. There are a growing number of companies working to provide solutions in this niche. With sufficient genetic data available, researchers can expand research and analysis to establish potential correlation of human biological nature (DNA sequencing), with many personal measurables, such as individual IQ. There are academic works already that investigate, establish and analyze measurables for genetic evidence of continuous natural selection in humans, genome characteristics to predict educational attainment (2), correlation between the size of human brain and annual family income, and many others (3).

To quantify and to analyze the level of environmental influences, and to distinguish them from genetically predetermined factors, a dataset of relevant personal information that can characterize environmental influences has to be obtained from the same group of people which DNA sequencing dataset info is available. Today, there is no any kind of standardized, universal dataset for analysis of every measurable that may have some correlation. Such personal information comes from variety of questionnaires.
If any correlation between genome characteristics and personal job outcome exist, and personal job outcome data can be obtained via questionnaire from the same group of people whose DNA sequencing is available, there will be always an issue about statistical sufficiency and scientifically accurate representation of such naturally limited data.

Prevailing traditional scientific research stays within a dominant paradigm of environmental influences to human nature. Any “extension” of it towards genetically predetermined factors is difficult or impossible without enabling a possibility to quantify analysis of environmental influences to human nature. However, with improvement and cost reduction of DNA technologies, we keep witnessing a growing number of correlations established beyond that paradigm (4). Researchers keep looking for more characteristics of human nature that can be associated with personal genomics, and new metrics will be established to quantify potential associations. For a purpose to enable research of association of new measurables, such as genomic influence to a personal work outcome, a platform for pre-recording of personal work outcome in product/service format over the lifetime, with millions of records worldwide, may serve as a reliable basis for ongoing analysis, given the DNA sequencing will become affordable and available to everyone. In the near future, such statistically sufficient datasets will become a foundation for that.

Over the years, information collected in this platform will grow to a monumental historical massive, serving multiply purposes:

– Personalization of a material world built around us

– Lifetime records for individual memory, use, and passing to next generations

– Datasets for correlational analysis with genome datasets

1. https://www.mobihealthnews.com/content/nebula-genomics-longenesis-team-create-life-data-ecosystem-data-sharing-platforms

2. http://www.pnas.org/content/113/28/7774

3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087752/

4. https://www.nobascholar.com/chapters/72/download.pdf

Partial solutions

Partial solutions can be sought on the paths of the purpose (application) of research that make sense for participants. Genealogical applications take a lead today.

One of the partial solution is the I@Work application, aimed for the job market. It allows users to record their lifetime work outcomes (products and services they were working on). The prototype can be seen at iwo.bio. We believe there is a value for an individual, either employed or not, to keep records on what he or she had done at work in some organized format.

ASSOCIATION BETWEEN HUMAN BRAIN (HIPPOCAMPAL GRAY MATTER PROBABILITY) AND INCOME

Prior Art

In the new era where we are moving into, various types of data will be collected over the lifetime. Today’s “life data ecosystem” concept is aimed to build a platform to establish an economy for exchanging genetic data and other biological information.

I@Work is proposing users to enter their records as a donation. To incentivize users, 1st Startup USA will provide users in-app DNA sequencing promotions and partnership perks from biotech firms, in exchange to the access for biotech researchers to users job outcome related data. For those who prefer to keep their data confidential, anonymous sequencing is available. For researchers, this solution will enable side-by-side DNA datasets with the relevant parameters of the same statistically sufficient massive of I@Work users that implicitly become research participants.