NMDC brings unique value to environmental microbial research by
But NMDC is not the source of any of that data or metadata, and the NMDC schema is strongly influenced by external standards and ontologies.
We have developed the NMDC schema to ensure that our metadata is FAIR, meaning that once a user has basic familiarity with the Data Portal or our APIs, they should be able to retrieve metadata about study X as easily as they can retrieve metadata about study Y. They should also be able to generate trustworthy summaries of the metadata across studies. None of that should require any prior familiarity with any of those studies.
NMDC launched a Value Set Squad in August 2024 to formalize which ontology terms (especially from EnvO) are suitable for
populating MIxS' env_broad_scale, env_local_scale and env_medium fields, in the context of a particular MIxS
Extension (aka environment). See more below.
We haven't completely achieved that goal yet, which means that some NMDC team members need the ability to retrieve the external metadata in their native form, analyze them systematically, and possibly integrate subsets of two or more metadata sources. We also need the ability to compare these external metadata against the schemas that accompany them.
The interpretation of metadata can be especially challenging when the values that are allowed for a particular slot or field come from some hierarchy in an ontology.
There is no shortage of expertise on our various metadata sources within NMDC. However, if person A asks their colleague B about some metadata, and B does some web searches followed by a mashup analysis on their laptop, then A (or someone else) won't be in a better position to build on that in the future. That means that external metadata exploration requires automation and the use of machine-readable file formats. This doesn't mean that B's web searching skills aren't valuable. It just means that B and A are responsible for converting the successful web search strategy into something that can be automated.
This repo uses the following technologies:
yq and runoakEveryone is welcome to contribute to the Python scripts and Makefile targets, although that is not necessary.
The greatest contribution that an NMDC team member can make is to add value-added file to the contributed directory.
Ideally, these files would be in a machine-readable format like TSV, CSV, YAML, or JSON. Contributors who have XLSX
files or Google Sheets are asked to save them as TSV or CSV. Please be aware of any knowledge that is captured in a way
that would be lost upon conversion to TSV or CSV, like color coding or comments. See Mark for help.
Many LinkML-related tools like runoak generate TSV output even when CSV is requested. However, many of the LLM web
interfaces will accept CSV uploads but reject TSV uploads. In a similar vein, YAML files are usually easier to read, but
LLMs are more likely to accept JSON files. This means that conversion between formats is a common task in the Makefiles.
Large language models like ChatGPT can provide valuable insights about the metadata and their standards, but there are
many caveats about loading data and prompts into LLMs and about interpreting their output. NMDC team members are
encouraged to experiment with LLMs for metadata and schema exploration, even if it is through a web interface. However,
all use of LLMs in this repo will be automated with the llm command line tool.
While many vendors provide a free tier for using their LLMs, those are usually not sufficient for NMDC metadata/schema exploration tasks. That's partially due to the fact that the free tiers don't use the most advanced models, but also due to the fact that the free (or even less expensive) models are more limited in the number of tokens (words or portions of words) they will accept as input, and the number of tokens they will emit as a response.
https://artificialanalysis.ai/ provides a nice analysis of the available LLM vendors and models, along with some quality metrics, token limits, and pricing.
Your institution may provide some level of free access to the more advanced LLMs. Most BBOP team members have access to a LBL-paid OpenAI/GPT account for API access (but not web access). LBL also provides the CBORG multi-model interface https://cborg.lbl.gov/
The LLMs that Mark has found most useful are Anthropic Claude Sonnet 3.5 (200k tokens input), OpenAI GPT 4 and 4o ( 128k), and Google Gemini 1.5 pro (2M).
The time and financial cost of developing LLM methods is not trivial, and there's no guarantee that the results will be accurate, comprehensive, or repeatable. In the long run, the best use of LLMs may be in interpreting input data in order to generate code that solves a problem algorithmically.
NMDC follows the MIxS practice of requiring Biosamples to be annotated with a triad of environmental context fields, env_broad_scale, env_local_scale, and env_medium. The EnvO repo provides general guidance for using EnvO terms to fill these fields (and that guidance is mirrored very closely by the descriptions of the MIxS fields linked above).
For example, the guidance for env_broad_scale is to use an identifier for some subclass
of biome.
But MIxS recognizes that Biosamples come from a wide variety of environments, which it models
as Extensions. mediterranean sea biome
and broadleaf forest biome
are both subclasses of biome, but we intuitively wouldn't expect
a Soil sample to have a env_broad_scale annotation
of mediterranean sea biome, nor would we intuitively expect
a Water sample to have a env_broad_scale annotation
of broadleaf forest biome.
NMDC's prioritization of environments (as MIxS Extensions)
The problem here is that intuition shouldn't be required for either providing metadata about Biosamples, or for searching over Biosample metadata. NMDC should take a stance on which ontology terms are appropriate for each combination of a MIxS environmental context field and a MIxS Extension, and NMDC should provide metadata submission and search tools that are aware of those per-Extension guidance sets.
The construction of value sets is intended to support submitters in faithfully describing their Biosamples, even in the context of innovative science, while also supporting data searchers. There will inevitably be some tension between these two goals.
EnvO has 127 biome subclasses, including an aquatic biome with 55 subclasses of its own. Should we include all of
them in the Water/env_broad_scale value set? For how many of our Water-environment studies
will marine white smoker biome
be relevant? If it is relevant for one, should we all of its siblings in the value set too, or leave it
ragged/inconsistent?
Analogously, there are 71 subclasses of biome that are not subclasses of aquatic biome. Should we include all of
them in a Soil/env_broad_scale value set? Even subtropical moist broadleaf forest biome?
There have been multiple previous attempts to define NMDC value sets for these fields, either in a standalone manner, or in the context of something else like the highly curated but less machine-actionable GOLD ecosystem paths. The methods have been variously ML-based, completely manual, SPARQL-based, etc. The outputs of these efforts may be spread across many GitHub repos and Google Docs.
Value sets for the three MIxS environmental context fields, for the Soil environment, have been retained as static enumerations in the NMDC submission-schema.
Note that this repo provides tooling for manually reviewing the ontology terms that have been associated with each NMDC biosample.
In some cases it may be necessary to request new terms from EnvO (or another ontology) to reflect the true environmental context of a sample.
Tested on Ubuntu 20.04 and MacOS Sonoma 12.0.1. Not all dependencies are required for all tools in this repo.
local/.env, based on local/.env.templateExtensions with linkml-map instead of yq?
robot extract-like CLI yet. Would require custom Python scripting.