+This work is licensed under a Creative Commons Attribution 4.0 International License.
The primary objective of this GitHub page is to serve as a centralized repository for existing (meta)data standards. The purpose is to provide the international microbiological community with a comprehensive and easily accessible compilation of established standards, facilitating efficient navigation and utilization for researchers involved in collecting and submitting (meta)data to public repositories.
In line with the NFDI4Microbiota (“German National Research Data Infrastructure” ) (“NFDI4Microbiota”) project’s objectives, this page aims to address the challenges of microbial (meta)data accessibility and consistency. The efficient exchange of usable information between research groups, sequencing centers, and data repositories has been a long-standing issue. Measure 2.1 (M2.1) specifically focuses on maximizing data quality within the NFDI4Microbiota (“NFDI4Microbiota”) system by enforcing compliance with existing standards and identifying additional tailored data standards and metadata requirements.
Goals: By centralizing standard parameters for metadata, the project ensures that generated data is reproducible and comparable both spatially and temporally. To achieve this, two milestones have been set:
- defining data standards for different types of raw data, and ensuring their quality and reliability
- defining data standards for technical metadata, further
enhancing the consistency and usability of the collected metadata.
After additional consideration and overviews of the current literature, the creators of this GitHub repository also agreed that the following sections could and should be found here:
- examples of commonly used licenses under which researchers can deposit data
- brief description of use of Ontologies and how they help you describe your data
In the context of metadata quality standards in microbial science, two main categories are being considered:
These categories aim to encompass the necessary information that researchers collecting and submitting metadata to public repositories need to provide. By adhering to these standardized metadata categories, researchers can ensure the integrity and interoperability of their data, enabling effective collaboration and comparative analysis within the international microbiological community.
- Begin by reading the NFDI4Microbiota introduction, Standards and Policies information, and Goals
- Next, read the information regarding technical metadata standards section
- Third, read the biological/environmental metadata standards section
- Fourth if further explanation about licensing and ontologies is
required, read Licenses and ontologies
section
Figure 1. Outlines the key aspects considered for
determining minimal metadata standards that can be universally
applicable across various datasets and microbiomes. These aspects
encompass both technical and biological/environmental (Bio/Env)
considerations. The figure illustrates the comprehensive approach used
to establish minimal metadata standards for diverse research settings by
combining already established standards for differing data types and
biomes.
Figure 1. Flow Chart of Technical and
Biological/Environmental Metadata Standard Development
This flow chart illustrates the process of developing metadata standards
for both Technical and Biological/Environmental aspects. Technical
parameters are categorized based on data types, while Bio/Env parameters
are organized according to biome types. Additionally, specific
considerations, such as file type and host, are taken into account to
enhance the comprehensiveness of the standards.
The following data types were considered when establishing minimal technical metadata standards for M2.1:
- Genomes
- Amplicon
- Metagenomes
- Metagenome assembled genomes (MAGs)
- Transcriptomes
- Metatranscriptomes
- Proteomes
- Metaproteomes
- Metabolomes
Standard parameter considerations for FASTQ and FASTA formats are displayed in Figure 2. and Figure 3., respectively. Parameter applicability to different data types and the time of data generation (i.e., before sequencing or during data processing) are shown on the left and right, respectively.
Additionally, standards are being considered for data transfer and
data integrity to ensure quality is
maintained throughout various processes of data file exchange.
Figure 2. Overview of Minimal
Technical Metadata for FASTQ Files
This figure provides an overview of the minimal technical metadata
relevant to FASTQ files. The left side lists the applicability of
parameters to different data types, such as (meta)genome,
(meta)transcriptome, etc. On the right side, the time of metadata
generation is indicated.
Figure 3. Overview of Minimal
Technical Metadata for FASTA Files
This figure presents an overview of the minimal technical metadata
relevant to FASTA files. On the left side, the applicability of
parameters to different data types, including (meta)genome,
(meta)transcriptome, etc., is listed. The right side provides
information about the time of metadata generation.
Establishing a file-specific metadata standard list poses a significant challenge due to variations in file types across instruments used in metabolomic and proteomic analyses. Thus, researchers can find the metadata standards for each specific technology within corresponding links. This approach recognizes the complexities of defining comprehensive and universally applicable metadata standards that differ based on technology.
- 2.3.1. Genome Sequencing
- Genomic FASTQ
- Genomic FASTA
- 2.3.2. Amplicon
Sequencing
- Amplicon FASTQ
- 2.3.3. Metagenome
Sequencing
- Metagenome FASTQ
- Metagenome FASTA
- Metagenome assembled genome (MAG) FASTA
- 2.3.4. Transcriptome
Sequencing
- Transcriptome FASTQ
- Transcriptome FASTA
- 2.3.5. Metatranscriptome
Sequencing
- Metatranscriptome FASTQ
- Metatranscriptome FASTA
- 2.3.6. Proteome
sequencing
- Proteome
- Proteome - experimental protocol edition
- 2.3.7. Metaproteome sequencing
- 2.3.8. Metabolome
sequencing
- Metabolome
- Metabolome - experimental protocol edition
- 2.3.9. uVIGs
- uVIG FASTQ
- uVIG FASTA
- 2.3.10. BIOM or tabular
files
The work of the Data transfer and data integrity section focuses on:
- Examples of existing data transfer & data integrity checks
- Data integrity considerations by file type
Six microbiomes were considered to compile a minimal set of biological and environmental metadata standards. Environmental and biological parameters were identified as minimums applicable to individual biomes and/or hosts.
The Minimal Biological and Environmental microbiome metadata standards within M2.1 were collected to apply to the following biomes:
- Marine
- Terrestrial
- Terrestrial (constructed)
- Plant-associated
- Animal-associated
- Human-associated
- Microbe-associated
Tentative standard minimal biological and environmental parameter
considerations are displayed in Figure 4. Parameter
applicability to different biomes are shown on the left axis.
Figure 4. Tentative Minimal
Biological and Environmental Metadata.
This figure presents the division of minimal biological and environmental metadata into distinct categories. Site metadata includes specifications and environmental parameters related to the geographic sampling location, while sample material and host metadata provide information specific to host-associated systems. The applicability of these standards to different microbiomes is shown on the left. Additionally, conditional metadata standards encompass pertinent minimal cultivation information.
The references in the figure are from the following sources:
- Marine references:
- GSC MIxS: Water MIMS (“GSC MIxS: WaterMIMS”)
- ENA MMC: ENA Checklist: Marine Microalgae (“ENA Marine Microalgae Checklist; Checklist: ERC000043”)
- ENA Tara Oceans; Checklist: ERC000030 (“ENA Tara Oceans; Checklist: ERC000030”)
- GSC Minimum Information about any (x) Sequence (MIxS); ENA checklist: Water environment (“GSC MIxS Water; ENA Checklist: ERC000024”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- Terrestrial / Terrestrial(constructed)
- GSC MIxS: Miscellaneous Natural Or Artificial Environment MIMS (“GSC MIxS: MiscellaneousNaturalOrArtificialEnvironmentMIMS”)
- GSC MIxS: Sediment MIMS (“GSC MIxS: SedimentMIMS”)
- GSC MIXS: Soil MIMS (“GSC MIxS: SoilMIMS”)
- GSC MIxS: Wastewater Sludge MIMS (“GSC MIxS: WastewaterSludgeMIMS”)
- GSC MIxS: Built Environment MIMS (“GSC MIxS: BuiltEnvironmentMIMS”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- Plant-associated
- GSC MIxS: Plant-associated MIMS (“GSC MIxS: Plant-associatedMIMS”)
- GSC MIxS: Agriculture MIMS (“GSC MIxS: AgricultureMIMS”)
- GSC MIxS: Symbiont-associated MIMS (“GSC MIxS: Symbiont-associatedMIMS”)
- ENA MMC: ENA Checklist: Marine Microalgae (“ENA Marine Microalgae Checklist; Checklist: ERC000043”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- Animal-associated
- GSC MIxS: Host-associated MIMS (“GSC MIxS: Host-associatedMIMS”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- Human-associated
- MIMS: metagenome/environmental, human-associated; version 6.0 Package (“MIMS: Metagenome/Environmental, Human-Associated; Version 6.0 Package”)
- GSC MIxS human associated; ENA Checklist: ERC000014 (“GSC MIxS Human Associated; ENA Checklist: ERC000014”)
- GSC MIxS: Human-associated MIMS (“GSC MIxS: Human-associatedMIMS”)
- GSC MIxS: Human-gut MIMS (“GSC MIxS: Human-gutMIMS”)
- GSC MIxS: Human-oral MIMS (“GSC MIxS: Human-oralMIMS”)
- GSC MIxS: Human-skin MIMS (“GSC MIxS: Human-skinMIMS”)
- GSC MIxS: Human-vaginal MIMS (“GSC MIxS: Human-vaginalMIMS”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- U.S. Office of Management and Budget (OMB): About the Topic of Race (“U.s. Office of Management and Budget (OMB): About the Topic of Race”)
- Microbe-associated
- GSC MIxS: Miscellaneous Natural Or Artificial Environment MIMS (“GSC MIxS: MiscellaneousNaturalOrArtificialEnvironmentMIMS”)
- GSC MIxS: Sediment MIMS (“GSC MIxS: SedimentMIMS”)
- GSC MIXS: Soil MIMS (“GSC MIxS: SoilMIMS”)
- GSC MIxS: Wastewater Sludge MIMS (“GSC MIxS: WastewaterSludgeMIMS”)
- GSC MIxS: Microbial Mat Biofilm MIMS (“GSC MIXS: MicrobialMatBiofilmMIMS”)
- The environment ontology: contextualising biological and biomedical entities (Buttigieg et al. 2013)
- The minimum information about a genome sequence (MIGS) specification (Field et al. 2008)
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications (Yilmaz et al. 2011)
- A standard MIGS/MIMS compliant XML Schema: Toward the development of the Genomic Contextual Data Markup Language (GCDML) (Kottmann et al. 2008)
- Standard reporting requirements for biological samples in metabolomics experiments: environmental context (Morrison et al. 2007)
- Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea (Bowers et al. 2017)
- Roadmap for naming uncultivated Archaea and Bacteria (Murray et
al. 2020)
The categorization framework in Figure 5 should be considered when determining the applicable metadata standards for each dataset. This framework can serve as a valuable tool for connecting information about samples from marine, terrestrial, or engineered systems. Additionally, it facilitates the inclusion of cultivated samples, whether they were cultured from a commercially-available source or isolated from an environmental sample by the user.
To enhance searchability in downstream analyses, users can select
multiple environment categories if relevant. For instance, they may
choose both “marine” and “terrestrial” for a tidal flat site,
“engineered” and “terrestrial” for a greenhouse agricultural site, or
“engineered” and “marine” for a commercially-available culture initially
isolated from the ocean.
Figure 5. Tentative Categorization Framework for Biological/Environmental Metadata Requirements
This figure showcases a preliminary categorization framework to
establish minimal biological/environmental metadata requirements. The
framework connects host-associated systems to marine, terrestrial, or
engineered environments while enabling effective tracking of data
affiliated with cultivated samples. The structure should provide
valuable insights for organizing and comprehensively accessing diverse
datasets.
Figures 6 - 8 show examples of minimal biological/environmental
metadata applicability to different sample categorizations.
Figure 6.
Example of Categorizing a Human Gut-Associated and Cultivated Sample
with Applicable Minimal Metadata
This figure provides an illustrative example of the categorization
process for a human gut-associated and cultivated sample. It showcases
the minimal metadata that are applicable and relevant for this specific
sample type.
Figure
7. Example of Categorizing a Tidal Flat and Cultivated Sample with
Applicable Minimal Metadata
This figure presents a practical example of categorizing a tidal flat
cultivated sample, along with the relevant minimal metadata. The
illustration demonstrates how the proposed framework accommodates
overlapping environments, such as terrestrial and marine, specifically
for intertidal regions.
Figure
8. Example of Categorizing a Known Lab Cultured Sample with Applicable
Minimal Metadata
This figure presents an example of categorizing a known lab-cultured
sample, along with the corresponding minimal metadata. The
bidirectionality of the categorization framework is highlighted, as it
enables the linkage between known, commercially available cultures and
their original sample environments.
When depositing data to public repositories, researchers can use established licenses to set certain restrictions on its use or requiring certain acknowledgments when reusing it or publish it to the public domain without any limitations. Licensing your data under specific licenses enables other researchers to reuse your data (under certain conditions), without explicit permission from the data submitter. In any case, it is recommended to consider various factors before deciding upon a deed (license). Ethical, privacy, and security considerations may heavily influence the licensing process. The most common licenses under use were established by a US non-profit organization called Creative Commons (CC). We encourage the readers of this repository to visit their site and familiarize themselves with the process, logic, and use of licenses in detail. The CC homepage also holds the Frequently Asked Questions (FAQ) section. Here, we will only briefly describe some of the CC licenses. So, in the end, researchers should think about how they want other people to use their work and why they want to share their work in the first place before deciding upon a deed (license).
Commonly used licenses:
- CC-BY: Credit must be given to the creator.
- CC BY-SA: Credit must be given to the creator. Adaptations must be shared under the same terms.
- CC BY-NC: Credit must be given to the creator. Only noncommercial uses of the work are permitted.
- CC BY-NC-SA: Credit must be given to the creator. Only noncommercial uses of the work are permitted. Adaptations must be shared under the same terms.
- CC BY-ND: Credit must be given to the creator. No derivatives or adaptations of the work are permitted.
- CC BY-NC-ND: Credit must be given to the creator. Only noncommercial uses of the work are permitted. No derivatives or adaptations of the work are permitted.
- CC0: Public
domain dedication.
| Acronym | Explanation |
|---|---|
| BY | Credit must be given to the creator |
| SA | Adaptations must be shared under the same terms |
| NC | Only noncommercial uses of the work are permitted |
| ND | No derivatives or adaptations of the work are permitted |
| 0 | Public domain dedication |
CC-BY: When data is
deposited under this deed (license), and it becomes free to share and
free to redistribute, including commercially, in any format or medium.
It also allows the user to build upon or transform the data/material for
any purpose, including commercial purposes. The deed (license) requires
the data reuser to give appropriate credit to the submitter/data
generator. In addition, the reuser must also provide a link to the deed
(license) and disclose any changes made when licensing their work when
derived from work already under deed (license).
CC0: When using this deed (license), the data/material becomes a part of the public domain. That means that the data deposited can be copied, modified, distributed, and used even for commercial purposes, and the depositor/generator of the data waives their right to the work. The reuser of data does not need to seek the permission of the data/material submitter or generator.
Before discussing controlled vocabularies (ontologies), we first need to
talk about what they are and why you should care. In this section, we
will interchangeably use the terms controlled vocabulary and ontology.
Let us dial the clock back a few centuries when Latin was the lingua
franca of academics. It provided a link between knowledge and sciences
from different backgrounds and languages (not to mention periods), along
with consistency and clarity. So, no matter what language you spoke in
your everyday life, Latin was there to help you understand the work of
an academic who lived halfway across the globe or several centuries
before. The common ground enabled ideas to spread and be built upon
existing knowledge without the burden of translation.
Much like in the past, nowadays, controlled vocabularies allow for
almost seamless communication and knowledge transfer between researchers
and between researchers and computers. Ontologies provide a common
language by defining and standardizing terms used in a particular field
of research. That reduces the chance of miscommunication and
misunderstandings. Ontologies also make data adaptable to new research
and discoveries and can provide a deeper understanding and uncover
patterns and insights on the subject being explored. They also make
navigation through (almost) endless rows of data easier with defined
search patterns.
Furthermore, the best thing about ontologies is that they are still evolving. Several research groups are updating and defining new terms, classes, and subclasses of ontologies to keep up with new discoveries and knowledge. There are, of course, several edge cases where ontologies are not yet defined, or even multiple ontologies can apply. But we highly encourage the readers of this GitHub not to get disheartened by the multitude of websites that provide these controlled vocabularies.
Let us imagine a theoretical experiment where we are taking metagenomic
samples, and we would like the rest of the world to know (and also
perhaps our future self) where and how exactly we collected our samples.
This is something we can consider at the time of planning our
experiment. We know that we will be collecting metagenomic samples from
the rhizosphere from a forest in Germany and sequencing them using
Illumina sequencing technologies. With this, we can begin describing our
samples.
We hop on over to the EMBL-EBI Ontology Lookup Service
(OLS). As we (in this thought experiment)
are unfamiliar with the ontologies, we start the search for the broadest
description we can think of, and that is probably biome
[ENVO_00000428],
so that is what we do, type biome in the search engine and hit enter. We
are greeted with several results of the
search. We take a closer
look at biome
[ENVO_00000428],
and find that there are several subclasses of it, that can help us
describe the sample. As we think about it, we come to the conclusion,
that the next class that could describe our sample is terrestrial biome
[ENVO:00000446],
but that is to broad of a description, so the search continues. After
some clicking and searching, we discover, that there is a certain
subclass called woodland biome
[ENVO:01000175], and
even a further subclass of it called temperate woodland biome
[ENVO:01000221]. This
should adequately describe the broad sense of what kind of samples we
have. However, we are not yet happy with our search, as we have yet to
define where exactly our samples come from in the temperate woodland
biome. At the same time, we have exhausted the subclasses in our current
search, so we return to the main page. We type in the term
rhizosphere and hit
search. Based on the result, we see that the rhizosphere environment
[ENVO:01000999] might
be something that could describe our sample, but let us take a closer
look at the description. The description reads Ontology which reads: “An
environmental system determined by the presence of a plant
rhizosphere.”. So, we have defined the origin of our sample.
A similar logic can be applied and used to let the world know that the
sampling was done in Germany, in the Naturpark Frankenwald
[GAZ:00632507], that we
are using Illumina Sequencing
[NCIT:C146817], that we
used minimal defined medium
[MCO:0000881], etc.
Note here that we highly encourage the readers of this repository to read the EnvO s use documentation if the examples given here are unclear.
Bowers, R., N. Kyrpides, R. Stepanauskas, et al. 2017. “Minimum Information about a Single Amplified Genome (MISAG) and a Metagenome-Assembled Genome (MIMAG) of Bacteria and Archaea.” Nat Biotechnol 35: 725–31. https://doi.org/10.1038/nbt.3893.
Buttigieg, P. L., N. Morrison, B. Smith, C. J. Mungall, S. E. Lewis, and ENVO Consortium. 2013. “The Environment Ontology: Contextualising Biological and Biomedical Entities.” Journal of Biomedical Semantics 4 (1): 43. https://doi.org/10.1186/2041-1480-4-43.
“ENA Marine Microalgae Checklist; Checklist: ERC000043.” https://www.ebi.ac.uk/ena/browser/view/ERC000043.
“ENA Tara Oceans; Checklist: ERC000030.” https://www.ebi.ac.uk/ena/browser/view/ERC000030.
Field, D., G. Garrity, T. Gray, N. Morrison, J. Selengut, P. Sterk, T. Tatusova, et al. 2008. “The Minimum Information about a Genome Sequence (MIGS) Specification.” Nature Biotechnology. 2008. https://doi.org/10.1038/nbt1360.
“German National Research Data Infrastructure.” https://www.nfdi.de/?lang=en.
“GSC MIxS Human Associated; ENA Checklist: ERC000014.” https://www.ebi.ac.uk/ena/browser/view/ERC000014.
“GSC MIxS Water; ENA Checklist: ERC000024.” https://www.ebi.ac.uk/ena/browser/view/ERC000024.
“GSC MIxS: AgricultureMIMS.” https://genomicsstandardsconsortium.github.io/mixs/AgricultureMIMS/.
“GSC MIxS: BuiltEnvironmentMIMS.” https://genomicsstandardsconsortium.github.io/mixs/BuiltEnvironmentMIMS/.
“GSC MIxS: Host-associatedMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Host-associatedMIMS/.
“GSC MIxS: Human-associatedMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Human-associatedMIMS/.
“GSC MIxS: Human-gutMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Human-gutMIMS/.
“GSC MIxS: Human-oralMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Human-oralMIMS/.
“GSC MIxS: Human-skinMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Human-skinMIMS/.
“GSC MIxS: Human-vaginalMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Human-vaginalMIMS/.
“GSC MIXS: MicrobialMatBiofilmMIMS.” https://genomicsstandardsconsortium.github.io/mixs/MicrobialMatBiofilmMIMS/.
“GSC MIxS: MiscellaneousNaturalOrArtificialEnvironmentMIMS.” https://genomicsstandardsconsortium.github.io/mixs/MiscellaneousNaturalOrArtificialEnvironmentMIMS/.
“GSC MIxS: Plant-associatedMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Plant-associatedMIMS/.
“GSC MIxS: SedimentMIMS.” https://genomicsstandardsconsortium.github.io/mixs/SedimentMIMS/.
“GSC MIxS: SoilMIMS.” https://genomicsstandardsconsortium.github.io/mixs/SoilMIMS/.
“GSC MIxS: Symbiont-associatedMIMS.” https://genomicsstandardsconsortium.github.io/mixs/Symbiont-associatedMIMS/.
“GSC MIxS: WastewaterSludgeMIMS.” https://genomicsstandardsconsortium.github.io/mixs/WastewaterSludgeMIMS/.
“GSC MIxS: WaterMIMS.” https://genomicsstandardsconsortium.github.io/mixs/WaterMIMS/.
Kottmann, R., T. Gray, S. Murphy, L. Kagan, S. Kravitz, T. Lombardot, D. Field, F. O. Glöckner, and Genomic Standards Consortium. 2008. “A Standard MIGS/MIMS Compliant XML Schema: Toward the Development of the Genomic Contextual Data Markup Language (GCDML).” OMICS: A Journal of Integrative Biology. 2008. https://doi.org/10.1089/omi.2008.0A10.
“MIMS: Metagenome/Environmental, Human-Associated; Version 6.0 Package.” https://www.ncbi.nlm.nih.gov/biosample/docs/packages/MIMS.me.human-associated.5.0/.
Morrison, Norman, Daniel Bearden, Jacob G. Bundy, Timothy Collette, Fraser Currie, Matthew Davey, Migdalia Dominguez, et al. 2007. “Standard Reporting Requirements for Biological Samples in Metabolomics Experiments: Environmental Context.” Metabolomics 3 (2): 203–10. https://doi.org/10.1007/s11306-007-0067-1.
Murray, A. E., J. Freudenstein, S. Gribaldo, et al. 2020. “Roadmap for Naming Uncultivated Archaea and Bacteria.” Nat Microbiol 5: 987–94. https://doi.org/10.1038/s41564-020-0733-x.
“NFDI4Microbiota.” https://nfdi4microbiota.de/.
“U.s. Office of Management and Budget (OMB): About the Topic of Race.” https://www.census.gov/topics/population/race/about.html.
Yilmaz, Pelin et al. 2011. “Minimum Information about a Marker Gene Sequence (MIMARKS) and Minimum Information about Any (x) Sequence (MIxS) Specifications.” Nature Biotechnology 29 (5): 415–20. https://doi.org/10.1038/nbt.1823.