Merge pull request #510 from jliermann/rework_domains

Rework domains

Author: jliermann

docs/00_intro/00_intro.mdx

@@ -30,11 +30,10 @@ At least and most importantly a loss of previously acquired data is always an [i
   width="max(500px, 50%)"
 />
 
-The NFDI4Chem knowledge base provides information and recommendations to digitalise all key steps of chemical research to support scientists in their efforts to collect, store, process, analyse, publish, and reuse research data. 
+The NFDI4Chem knowledge base provides information and recommendations to digitalise all key steps of chemical research to support scientists in their efforts to collect, store, process, analyse, publish, and reuse research data.
 
 Actions to [promote Open Science and Research Data Management](https://riojournal.com/article/55852/) in accordance with the FAIR data principles are presented by everyday users and range from planning and implementation to publication and reuse.
 
-
 :::info Guidance for getting started:
 The knowledge base offers different points of entry that help you in navigating the site and simplify the targeted search for information. Start with the search (Ctrl+K) or use the main navigation bar for topics like domains, roles, handling data articles, smartlab, or publication of data.
 :::
@@ -89,6 +88,6 @@ In this category on data publishing you will find all the important information
   text={"Data Publishing"}
 />
 
-:::Acknowlegdements
+:::info Acknowledgements
 This knowledge base is inspired by [RDMkit](https://rdmkit.elixir-europe.org/index.html) but has been tailored specifically towards Chemists as end-users.
 :::

docs/00_intro/10_fair.mdx

@@ -3,8 +3,6 @@ title: "FAIR Data Principles"
 slug: "/fair"
 ---
 
-# FAIR Data Principles
-
 ![FAIR Data](/img/topics/FAIR_data_principles.png)
 
 Image Attribution: SangyaPundir, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0).
@@ -30,19 +28,19 @@ Researchers — and the computers working on their behalf — must be able to fi
 
 ### F1. (meta)data are assigned a globally unique and persistent identifier {#f1}
 
-A globally unique and [persistent identifier (PID)](/docs/pid) helps both machines and humans find the data in the first place. These PIDs are essential for research as they guarantee the availability of the associated resource, in this case a dataset. The registry services that make these identifiers available work to maintain the link to the resource, thus avoiding dead links. 
+A globally unique and [persistent identifier (PID)](/docs/pid) helps both machines and humans find the data in the first place. These PIDs are essential for research as they guarantee the availability of the associated resource, in this case a dataset. The registry services that make these identifiers available work to maintain the link to the resource, thus avoiding dead links.
 
 A common example of a citable PID is the Digital Object Identifier, or [DOI](https://doi.org/10.1000/182). As with many journals, scientific data repositories often assign a DOI automatically. For example, both the [The Cambridge Structural Database (CSD)](https://www.ccdc.cam.ac.uk/solutions/csd-system/components/csd/) and the [Chemotion Repository](https://www.chemotion-repository.net/) assign DOIs to each dataset deposited. Researchers must be aware of this option when searching for a suitable repository (e.g. at [re3data](https://www.re3data.org/)), while repositories should offer this service.
 
 ### F2. data are described with rich metadata (defined by R1 below) {#f2}
 
 Data need to be sufficiently described in order to make them both findable and reusable. Hence, the specific focus here lies on making the (meta)data findable by using rich discovery [metadata](/docs/metadata) in a standardized format and allowing computers and humans to quickly understand the dataset’s contents. This is an essential component in the plurality of metadata described by [R1](#r1) below. This information may include, but is not limited to:
 
--   the context on what the dataset is, how it was generated, and how it can be interpreted,
--   the data quality,
--   licensing and (re)use agreements,
--   what other data may be related (linked via its PID), and
--   associated journal publications and their DOI.
+- the context on what the dataset is, how it was generated, and how it can be interpreted,
+- the data quality,
+- licensing and (re)use agreements,
+- what other data may be related (linked via its PID), and
+- associated journal publications and their DOI.
 
 Repositories should provide a fillable [application profile](https://en.wikipedia.org/wiki/Application_profile) that allows researchers to give extensive and precise information on their deposited datasets. For example, the Chemotion Repository uses and [RADAR4Chem](https://www.nfdi4chem.de/index.php/2650-2/), among others, the [Datacite Metadata Schema](http://doi.org/10.5438/0012) to build its application profile, a schema specifically created for the publication and citation of research data. These include an assortment of mandatory, recommended, and optional metadata properties, allowing for a rich description of the deposited dataset. For those publishing data, always keep in mind: the more information provided, the better.
 
@@ -106,17 +104,17 @@ Many of the previous points lead to one key aspect of data sharing: data reusabi
 
 Related to [F2](#f2):
 
--   What the dataset contains, including whether the data is raw and/or processed
--   How the data was processed
--   How the data can be reused
--   Who created the data
--   Date of creation
--   Variable names
--   Standard methods used
--   Scope of the data and project
--   Lab conditions
--   Any limitations to the data
--   Software and versions used for acquisition and processing.
+- What the dataset contains, including whether the data is raw and/or processed
+- How the data was processed
+- How the data can be reused
+- Who created the data
+- Date of creation
+- Variable names
+- Standard methods used
+- Scope of the data and project
+- Lab conditions
+- Any limitations to the data
+- Software and versions used for acquisition and processing.
 
 An important piece of information for chemical data are [machine-readable chemical structures](/docs/machine-readable_chemical_structures). This should be included within the dataset and/or metadata and aids computers in finding the correct data in their queries.
 
@@ -140,8 +138,8 @@ Where required, format converters should be linked in the dataset’s metadata.
 
 ## Sources and further information
 
--   [FORCE 11: FAIR Data Principles](https://www.force11.org/group/fairgroup/fairprinciples)
--   [Go-FAIR initiative: FAIR Principles](https://www.go-fair.org/fair-principles/)
--   [TIB Blog: The FAIR Data Principles for Research Data](https://blogs.tib.eu/wp/tib/2017/09/12/the-fair-data-principles-for-research-data/)
--   [FAIRsFAIR: How to be FAIR with your data. A teaching and training handbook for higher education institutions](https://doi.org/10.5281/zenodo.6674301) & [Engelhardt et al. (book version)](https://doi.org/10.17875/gup2022-1915) & [Gitbook version](https://fairsfair.gitbook.io/fair-teaching-handbook)
--   [Checklist: How FAIR are your data?](https://doi.org/10.5281/zenodo.1065991) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1065991.svg)](https://doi.org/10.5281/zenodo.1065991)
+- [FORCE 11: FAIR Data Principles](https://www.force11.org/group/fairgroup/fairprinciples)
+- [Go-FAIR initiative: FAIR Principles](https://www.go-fair.org/fair-principles/)
+- [TIB Blog: The FAIR Data Principles for Research Data](https://blogs.tib.eu/wp/tib/2017/09/12/the-fair-data-principles-for-research-data/)
+- [FAIRsFAIR: How to be FAIR with your data. A teaching and training handbook for higher education institutions](https://doi.org/10.5281/zenodo.6674301) & [Engelhardt et al. (book version)](https://doi.org/10.17875/gup2022-1915) & [Gitbook version](https://fairsfair.gitbook.io/fair-teaching-handbook)
+- [Checklist: How FAIR are your data?](https://doi.org/10.5281/zenodo.1065991) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1065991.svg)](https://doi.org/10.5281/zenodo.1065991)

docs/10_domains/00_domain_guide.mdx

@@ -9,18 +9,4 @@ import domains from "@site/src/data/domains";
 
 Looking for information on managing research data in chemistry? Choose your chemical domain and find discipline-specific help on research data management.
 
-:::info Info:
-The domain-specific profiles provide an overview of the individual RDM steps. The following topics are presented in detail:
-
-- summary of domain-specific profile
-- type of experiments
-- planning of experiments
-- documentation of experiments
-- data producing methods (including a table with recommendations on interoperable open file formats)
-- data analysis
-- publication of research data
-  :::
-
-Click on a button below to get started with your chemical domain. The domain-specific profiles will be continuously updated based on new developments and feedback.
-
 <Features featureList={domains} />

docs/10_domains/10_analytical_chemistry.mdx

@@ -12,7 +12,7 @@ import ElnFinder from "@site/src/components/eln/ElnFinder.js";
 
 ## Introduction
 
-Although analytical chemistry is one of the oldest branches of chemistry, it continues to evolve. New methods and technologies are constantly being developed. Providing the tools and techniques needed to identify and quantify the chemical constituents of a sample, analytical chemistry is a cornerstone of both academia and industry. It is essential for a wide range of applications, from environmental monitoring to drug discovery.
+Providing the tools and techniques needed to identify and quantify the chemical constituents of a sample, analytical chemistry is a cornerstone of both academia and industry. It is essential for a wide range of applications, from environmental monitoring to drug discovery.
 
 Despite the diversity of analytical methods, a common denominator is the large amount of data generated by instrumental methods. This data must be processed and interpreted to extract meaningful information. This makes analytical chemistry a challenging field for research data management.
 
@@ -22,25 +22,23 @@ Unlike some other areas of chemistry, most research data in analytical chemistry
 
 Techniques like mass spectrometry, chromatography, and spectroscopy generate complex data consisting of raw data files, metadata, and processed data. Raw data files are often proprietary and require specialised software to open and interpret. Metadata is crucial for understanding experimental conditions and parameters. Processed data can range from simple peak lists to complex multivariate models.
 
-Some open data formats are available for specific data types, such as mass spectrometry data in the [mzML format](https://www.psidev.info/mzML). The [JCAMP-DX format](https://iupac.org/what-we-do/digital-standards/jcamp-dx/) is used for optical spectroscopy data. This format is also suitable for NMR spectroscopy data, but with some major limitations. For chromatography or combined chromatography-mass spectrometry data the situation is more complex as many vendors have their own proprietary formats.
+Some open data formats are available for specific data types, such as mass spectrometry data in the [mzML format](https://www.psidev.info/mzML). The [JCAMP-DX format](https://iupac.org/what-we-do/digital-standards/jcamp-dx/) is used for optical spectroscopy data. This format is also suitable for NMR spectroscopy data, but with some major limitations. For chromatography or combined chromatography-mass spectrometry data, the situation is more complex, as many vendors have their own proprietary formats.
 
 ## ELNs and Other Tools
 
-For effective data management, software tools should be selected in a uniform manner within a project or research group with the aim to [organize](/docs/data_organisation)
-and streamline workflows. This involves establishing clear usage guidelines, including metadata templates drawn from minimum information standards for a given method, where available.
-These should be outlined in a [data management plan (DMP)](/docs/dmp) for each project. NFDI4Chem provides an [RDMO template](https://rdmo.nfdi4chem.de/) specifically tailored to the needs of chemists.
+For effective data management, tools should be selected at project or group level based on workflows. Because workflows are often method-specific, usage guidelines and metadata templates should be defined and documented in a [data management plan (DMP)](/docs/dmp). NFDI4Chem provides an [RDMO template](https://rdmo.nfdi4chem.de/) tailored to chemistry.
 
-General chemistry ELNs can typically be used for analytical chemistry data and may be well suited to your research topic. However, there are also specialised tools that are tailored to the needs of analytical chemists. These tools often include features for managing instrument data, processing raw data files and visualising results. They may also include tools for chemometric analysis.
+General chemistry ELNs can typically be used for analytical chemistry data and may be well suited to your research topic. However, there are also specialised tools tailored to the needs of analytical chemists. These tools often include features for managing instrument data, processing raw data files, and visualising results. They may also include tools for chemometric analysis.
 
 <ElnFinder subDisc="Analytical chemistry" />
 
 ## Publishing Data
 
 Data from analytical chemistry can be published on several platforms, depending on the research subject and data type.
 
-If the analytical data have a more supporting role in a larger study it may be appropriate to publish the data in a general data repository. If the research focuses more on the analytical method itself, it may be more appropriate to publish the data in a specialised repository.
+If the analytical data have a supporting role in a larger study, it may be appropriate to publish them in a general data repository. If the research focuses on the analytical method itself, a specialised repository may be more appropriate.
 
-General data repositories include [Zenodo](https://zenodo.org/) or [RADAR4Chem](https://radar.products.fiz-karlsruhe.de/de/radarabout/radar4chem). For analytical data in context with synthetic chemistry data, [Chemotion Repository](https://chemotion-repository.de/) might also be a suitable option.
+General data repositories include [RADAR4Chem](https://radar.products.fiz-karlsruhe.de/de/radarabout/radar4chem) or [Zenodo](https://zenodo.org/). For analytical data in context with synthetic chemistry data, [Chemotion Repository](https://chemotion-repository.de/) might also be a suitable option.
 
 For method-specific data, several specialised repositories are available. A few examples include: