Delsa Workshop IV: Launching the Quantified Human Initiative
Bibliographic record
Abstract
The mission of the Data-Enabled Life Sciences Alliance (DELSA Global) is to ''Accelerate the impact of data-enabled life science research on the pressing needs of the global society.''In its first 18 months, DELSA has catalyzed connections and interactions for more effective and sustainable science by bringing stakeholders together through physical or virtual proximity to share ideas, discuss new insights, and form novel collaborations.During our most recent annual Washington, DC, meeting (May 16-17, 2013), DELSA brought together life and computer scientists, data analysts, research funding agency representatives, and many others to discuss and formulate plans for furthering the initiative of 21st-century collective innovation.In an exciting day of lightning talks and brainstorming, participants discussed the management and analysis of emerging datasets that hold such immense promise for understanding and improving the human condition and our relationship with the worlds around us and within us.A focus of this meeting was on the Quantified Human (QH) Initiative.QH takes our natural curiosity about self and combines multi-omics and clinical data to draw conclusions about our physical condition both current and future.Measures such as height, weight, and blood pressure have been used throughout medical history; however, it is now possible to track many other measures such as caloric/nutritional intake and output, blood components, and sleep patterns.These data can be viewed in the context of our body as an ecosystem by including measures of the commensal microorganisms, collectively referred to as the microbiome.All of these results, taken together and over a period of time, can lead to a detailed picture of our overall health and open up a whole new level of understanding about the microenvironment that exists inside us.However, the resulting datasets are complex and immense.While the potential exists to use these data to explore the depth and breadth of ourselves in new and unimagined ways, we need new paradigms and policies for organizing, managing, and sharing the data, combined with new publishing and citation models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".