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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.
The NFDI4Microbiota is a consortium that is part of the German National research Data Infrastructure (NFDI). In line with the consortium’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 "Data and Metadata Standards") specifically focuses on maximizing data quality within the NFDI4Microbiota consortium 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. Virus Genomes
- Virus genome FASTQ/A
- 2.3.11. 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”)
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 diving into how controlled vocabularies (also known as ontologies) are used, let us first clearly understand what they are and why they are important. Throughout this section, we will use the terms controlled vocabulary and ontology interchangeably.
Imagine traveling back a few centuries to a time when Latin was the common language among scholars. Regardless of their native language or culture, scientists and scholars used Latin to communicate their ideas clearly and consistently. This shared language allowed ideas to spread widely, bridging gaps in distance, language, and even time.
In a similar way, today we use controlled vocabularies to create a common language among researchers, and even between humans and computers. Ontologies standardize the meaning of terms within a specific scientific field, reducing confusion and making communication clearer. They help scientists clearly organize data, easily navigate large datasets, and discover new patterns or insights. Moreover, ontologies evolve continuously. New terms, definitions, and classifications are regularly added as our scientific knowledge expands.
Sometimes it can feel overwhelming because multiple ontologies exist or a particular term has not yet been clearly defined. But do not be discouraged! As you gain experience, using controlled vocabularies will become second nature.
To understand how ontologies can be practically applied, let us walk through an imaginary scenario. Suppose you are planning an experiment to collect metagenomic samples. You want to clearly record details such as where the samples were taken and how the data was obtained. For this example, let us say you collected your samples from the rhizosphere (the soil surrounding plant roots) of a forest in Germany and used Illumina sequencing technology for analysis. How would you clearly and precisely document these details?
First, you would visit an ontology service, such as the EMBL-EBI Ontology Lookup Service (OLS) or OBO foundry. Since you're just starting out, you might begin with a broad term like biome [ENVO_00000428]. By searching for "biome," you’ll find a list of related terms. One of these subclasses is terrestrial biome [ENVO:00000446]. But this might still be too broad, so you'll keep searching. After exploring further, you'll find the subclass woodland biome [ENVO:01000175], and eventually the more specific subclass temperate woodland biome [ENVO:01000221]. This accurately describes the general environment of your sample. This accurately describes the general environment of your sample.Next, you might look up the more specific term rhizosphere to pinpoint the exact origin of your sample. Searching for rhizosphere returns a useful term: rhizosphere environment [ENVO:01000999], defined as "an environmental system determined by the presence of a plant rhizosphere." Perfect! Now you've precisely defined the exact location of your sample collection.
Similarly, you can specify that your samples were collected in Germany at Naturpark Frankenwald [GAZ:00632507], that your sequencing was done with Illumina Sequencing [NCIT:C146817], and even describe that you used a minimal defined medium [MCO:0000881].
If you find the examples here challenging or want more information, we strongly recommend visiting the EnvO s use documentation which provides more detailed guidance.
Clearer examples of onotolgy uses for different Biomes and environments can also be found in this GitHub page for seven different considered biomes.
It is however, also important to note, that in some cases some metadata annotations can not be possible with ontologies, values or free text, as the metadata fields is either not applicable to the project, was not collected or recorded or simply not provided due to ethical and legal resons. These different reasons for an absent value are explained in the table bellow.
| Phrase | Reason for use | When does it apply | INSDC token | DataCite code | ISO/GML (nilReason) | Definition |
|---|---|---|---|---|---|---|
| not applicable; not relevant |
Field is outside the scope of the experiment | Depth for human stool sample | not applicable for a control missing: control sample |
:unap | inapplicable | Information is inappropriate to report; sometimes shows a gap in the standard itself. |
| not available | Value exists somewhere, but you cannot obtain it | Host BMI missing from a 1990 gut study | Top level: missing or: missing: third party data |
:unav | missing | Information of an expected format was not given because it is unavailable or unretrievable, with no expectation of later supply. |
| not recorded | Measurement was never captured | Ambient temperature not recorded during environmental swabbing | missing: not collected missing: lab stock |
:unav | missing | Information was expected but never collected at source. |
| not provided | Value exists but is under embargo / pending | Exact collection date withheld until publication of the clinical trial | not provided | :tba | template or other:pending |
Information of an expected format was not given now, but will be supplied later. |
| restricted access | Value exists but must remain confidential | Coordinates of endangered species or patient postcode that could be used to identify individual | restricted access | :unal or :unac if the restriction is temporary |
withheld | Information exists but cannot be released openly because of privacy, conservation or legal constraints. |
Sources:
INSDC Missing Value Reporting Terms;
DataCite - Appendix 3: Standard values for unknown information;
ISO 19115-3
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.