New structure

docs/00_intro/10_fair.mdx

@@ -5,7 +5,7 @@ slug: "/fair"
 
 ![FAIR Data](/img/topics/FAIR_data_principles.png)
 
-Image Attribution: SangyaPundir, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0).
+_Image Attribution: SangyaPundir, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0)._
 
 ## Introduction
 

docs/00_intro/20_data_life_cycle.mdx

@@ -26,7 +26,7 @@ You should be aware of the various **requirements** of the research funder, your
 
 ## Phase 2: Experiment/ Data Collection
 
-**New data** is created or collected in this phase, e.g. through chemical synthesis, simulations of molecules, or measurements carried out on samples. When conducting the respective experiment, make sure that you also create or record the **relevant [metadata](/docs/metadata)** that describes the data. The [documentation](/docs/data_documentation) of the created data with all further information and metadata should be recorded in an [(electronic) laboratory notebook](/docs/eln).
+**New data** is created or collected in this phase, e.g. through chemical synthesis, simulations of molecules, or measurements carried out on samples. When conducting the respective experiment, make sure that you also create or record the **relevant [metadata](/docs/metadata)** that describes the data. The [annotation](/docs/data_description_annotation) of the created data with all further information and metadata should be recorded in an [(electronic) laboratory notebook](/docs/eln).
 
 If you use existing data or samples, **clarify the rights of use**. In this case, provenance metadata may provide you with information. In addition, a link should be created between the existing data and the newly generated data. This is relevant information and helps you in the [reuse phase](#phase-6-re-use).
 

docs/40_smartlab/00_smartlab.mdx → docs/00_intro/30_smartlab.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Smartlab"
+title: "SmartLab"
 slug: "/smartlab"
 id: "smartlab"
 ---

docs/20_role/10_research_group_leader.mdx

@@ -27,7 +27,7 @@ Digitisation of research data only **after** the end of the production process i
 
 Therefore, it is more efficient to capture the data and their corresponding [metadata](/docs/metadata) as early as from the planning of the experiment. Here, [electronic lab notebooks (ELN)](/docs/eln) facilitate everyday work considerably: the planning of the experiment, the documentation of experimental procedures, the analysis of the obtained spectroscopic data as well as the peak assignment can all be completed in one digital environment. And even better: complete experiment reports with analytical data (e.g. for the supporting information for [publications](/docs/data_publishing)) can be generated automatically by the ELN.
 
-The time invested to set up the ELN and to organise the experiments thus pays off in numerous ways: In addition to facilitating [documentation](/docs/data_documentation), the storage of the produced data in a FAIR format in a [repository](/docs/repositories) is simplified. Also, the [data organisation](/docs/data_organisation) for the research group can be improved by setting up an internal database. This is invaluable for growing working groups.
+The time invested to set up the ELN and to organise the experiments thus pays off in numerous ways: In addition to facilitating [annotation](/docs/data_description_annotation), the storage of the produced data in a FAIR format in a [repository](/docs/repositories) is simplified. Also, the [data organisation](/docs/data_organisation) for the research group can be improved by setting up an internal database. This is invaluable for growing working groups.
 
 ## How to start
 

docs/20_role/20_research_group_member.mdx

@@ -11,7 +11,7 @@ This article applies to research group members (PhD students, Postdocs and other
 
 ## Motivation
 
-As a research group member, you are the one who is doing the actual research. That means, you are generating lots of data while working practically or theoretically, e.g. analytical data or reaction protocols. All these data are accompanied by [metadata](/docs/metadata). As a scientist, you are responsible for the [documentation](/docs/data_documentation) of this data in order to ensure that the data can be archived in a [FAIR](/docs/fair) (**F**indable, **A**ccessible, **I**nteroperable, **R**e-usable) manner. Many funding institutions encourage or even enforce the publication of FAIR data. Therefore, your [supervisor](/docs/research_group_leader) should provide you with information on how to handle your data or even a [data management plan](/docs/dmp) for good [data organisation](/docs/data_organisation) practices.
+As a research group member, you are the one who is doing the actual research. That means, you are generating lots of data while working practically or theoretically, e.g. analytical data or reaction protocols. All these data are accompanied by [metadata](/docs/metadata). As a scientist, you are responsible for the [annotation](/docs/data_description_annotation) of this data in order to ensure that the data can be archived in a [FAIR](/docs/fair) (**F**indable, **A**ccessible, **I**nteroperable, **R**e-usable) manner. Many funding institutions encourage or even enforce the publication of FAIR data. Therefore, your [supervisor](/docs/research_group_leader) should provide you with information on how to handle your data or even a [data management plan](/docs/dmp) for good [data organisation](/docs/data_organisation) practices.
 
 ## Data handling
 
@@ -23,7 +23,7 @@ As a research group member, you are the one who is doing the actual research. Th
 
 In recent years, many new digital tools have been developed to support researchers with their RDM needs. The technical possibilities are briefly explained below. For more details, please refer to the linked related chapters. If you want to learn more about domain-specific data production methods, have a look at the [domain-specific profiles](/docs/role_guide).
 
-The most efficient way to properly handle your research data and corresponding [metadata](/docs/metadata) starts when planning the experiment according to the [data life cycle](/docs/data_life_cycle) (see figure on the right). In order to have all data directly available in a digital format, the use of [electronic lab notebooks (ELNs)](/docs/eln) is strongly recommended. ELNs can support you in your daily work, from planning experiments to data processing and analysis. For example, after peak assignment in an obtained spectroscopic dataset—which can be performed entirely in one digital environment—peak lists can automatically be generated by ELNs such as the [Chemotion ELN](https://www.chemotion.net/) for theses or publications. Furthermore, ELNs are very powerful in preparing your data for [data publishing](/docs/data_publishing) in suitable [repositories](/docs/repositories) as well as for [data storage and archiving](/docs/data_storage). You are therefore facilitating future research in your working group by assembling an internal database in an invaluable manner. In addition to the group you are working in, publishing [well-organised](/docs/data_organisation) and [documented](/docs/data_documentation) research data can help other researchers significantly in terms of searching and re-using your data. [Best practice examples](/docs/best_practice) can guide you to better understand how efficient data handling works.
+The most efficient way to properly handle your research data and corresponding [metadata](/docs/metadata) starts when planning the experiment according to the [data life cycle](/docs/data_life_cycle) (see figure on the right). In order to have all data directly available in a digital format, the use of [electronic lab notebooks (ELNs)](/docs/eln) is strongly recommended. ELNs can support you in your daily work, from planning experiments to data processing and analysis. For example, after peak assignment in an obtained spectroscopic dataset—which can be performed entirely in one digital environment—peak lists can automatically be generated by ELNs such as the [Chemotion ELN](https://www.chemotion.net/) for theses or publications. Furthermore, ELNs are very powerful in preparing your data for [data publishing](/docs/data_publishing) in suitable [repositories](/docs/repositories) as well as for [data storage and archiving](/docs/data_storage). You are therefore facilitating future research in your working group by assembling an internal database in an invaluable manner. In addition to the group you are working in, publishing [well-organised](/docs/data_organisation) and [annotated](/docs/data_description_annotation) research data can help other researchers significantly in terms of searching and re-using your data. [Best practice examples](/docs/best_practice) can guide you to better understand how efficient data handling works.
 
 ## Recommendations – how to start
 

docs/20_role/30_student.mdx

@@ -20,15 +20,15 @@ If you want to learn more about domain-specific data production methods, you can
 ## Data documentation
 
 :::danger Why:
-The use of [well-organised](/docs/data_organisation) and [documented data](/docs/data_documentation) plays an important role in finding, searching, and using research data throughout the [data life cycle](/docs/data_life_cycle).
+The use of [well-organised](/docs/data_organisation) and [annotated data](/docs/data_description_annotation) plays an important role in finding, searching, and using research data throughout the [data life cycle](/docs/data_life_cycle).
 :::
 
 :::info How:
-For the [documentation of these data](/docs/data_documentation), a classic paper-based lab journal or an [electronic lab journal](/docs/eln) (ELN) can be used.
+For the [description and annotation](/docs/data_description_annotation) of these data, a classic paper-based lab journal or an [electronic lab journal](/docs/eln) (ELN) can be used.
 :::
 
 :::caution Considerations:
-The use of a paper-based lab journal seems to be an easy and efficient way to [document data](/docs/data_documentation). But the use of an [ELN](/docs/eln) like [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) has plenty of advantages in the long run. For example, in [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) all data obtained from an experiment can be linked to your protocol, which is an easy way to have a [well-organised research data set](/docs/data_organisation) at the end of the thesis. Additionally, the reaction protocol can be exported in a [standardised form](/docs/data_formats), which is suitable for [publication](/docs/data_publishing) in the experimental section of a thesis or a paper. <br/> Furthermore, the data are machine readable and therefore easily findable which is one aspect for [FAIR data](/docs/fair) (**F**indable, **A**ccessible, **I**nteroperable, **R**eusable). Also the other aspects should be fulfilled, this increases the machine-actionability for future use of the data. Another advantage of an [ELN](/docs/eln) is that it can be coupled with a [repository](/docs/repositories), which will ease the [publication process](/docs/data_publishing). [Choose a repository](/docs/choose_repository), that is suitable for your data.
+The use of a paper-based lab journal seems to be an easy and efficient way to document data. But the use of an [ELN](/docs/eln) like [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) has plenty of advantages in the long run. For example, in [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) all data obtained from an experiment can be linked to your protocol, which is an easy way to have a [well-organised research data set](/docs/data_organisation) at the end of the thesis. Additionally, the reaction protocol can be exported in a [standardised form](/docs/data_formats), which is suitable for [publication](/docs/data_publishing) in the experimental section of a thesis or a paper. <br/> Furthermore, the data are machine readable and therefore easily findable which is one aspect for [FAIR data](/docs/fair) (**F**indable, **A**ccessible, **I**nteroperable, **R**eusable). Also the other aspects should be fulfilled, this increases the machine-actionability for future use of the data. Another advantage of an [ELN](/docs/eln) is that it can be coupled with a [repository](/docs/repositories), which will ease the [publication process](/docs/data_publishing). [Choose a repository](/docs/choose_repository), that is suitable for your data.
 :::
 
 ## Data processing
@@ -42,7 +42,7 @@ Data can be processed by different software for each method. But an [ELN](/docs/
 :::
 
 :::caution Considerations:
-By [documenting](/docs/data_documentation) and processing data in the same software tool synergies will be obtained. For example Chemspectra can control automatically, if the integral of the NMR spectra fits to the target molecule of the connected synthesis. After processing your data in Chemspectra you can export your data in a standardised peak list, which can be added in the experimental section of a thesis or a [publication](/docs/data_publishing).
+By documenting and processing data in the same software tool synergies will be obtained. For example Chemspectra can control automatically, if the integral of the NMR spectra fits to the target molecule of the connected synthesis. After processing your data in Chemspectra you can export your data in a standardised peak list, which can be added in the experimental section of a thesis or a [publication](/docs/data_publishing).
 :::
 
 ## Sharing, preserving and re-using data

docs/30_data/00_data_guide.mdx

@@ -23,11 +23,17 @@ Learn how to organise your data, e.g. via appropriate file naming conventions an
 
 <FeatureButton {...commonFeatures.dataOrganisation} />
 
-### Documentation
+### Data Formats
 
-Find out more about the tools, resources and software that can support you with data documentation.
+Learn about the different data formats and their advantages and disadvantages. Find out which formats are suitable for your data and how to convert between them.
 
-<FeatureButton {...commonFeatures.dataDocumentation} />
+<FeatureButton {...commonFeatures.dataFormats} />
+
+### Data Description and Annotation
+
+Learn how to describe and annotate your data to make it more discoverable and reusable.
+
+<FeatureButton {...commonFeatures.dataAnnotation} />
 
 ### Data Storage and Archiving
 

docs/30_data/30_data_organisation.mdx → docs/30_data/20_data_organisation.mdx

@@ -6,7 +6,7 @@ nfdi4chem-tags: [data_organisation, data_storage, repositories]
 slug: "/data_organisation"
 ---
 
-Besides [documentation of your research data](/docs/data_documentation), another aspect which requires attention is the way how data are named, organised, shared, and referenced during a research activity. Small amounts of data can be just organised in files and folders; when working with many or large data sets, it can be convenient to organise them in a database. For an idea about storage locations and storage methods see [Data Storage and Archiving](/docs/data_storage).
+Besides [description and annotation](/docs/data_description_annotation) of your research data, another aspect which requires attention is the way how data are named, organised, shared, and referenced during a research activity. Small amounts of data can be just organised in files and folders; when working with many or large data sets, it can be convenient to organise them in a database. For an idea about storage locations and storage methods see [Data Storage and Archiving](/docs/data_storage).
 
 ## Files: naming conventions
 
@@ -19,7 +19,7 @@ myfinaldata_V2.csv
 myfinaldata_V2_ready.csv
 ```
 
-We need short and descriptive file names as an identifier for a document. Useful names give information on what the files contain and helps in sorting them. Pay attention to your naming scheme when working with others: It is important to follow the same file naming convention, so develop an adapted scheme when the project starts and write it down including explaination of abbreviations in your [data documentation](/docs/data_documentation).
+We need short and descriptive file names as an identifier for a document. Useful names give information on what the files contain and helps in sorting them. Pay attention to your naming scheme when working with others: It is important to follow the same file naming convention, so develop an adapted scheme when the project starts and write it down including explaination of abbreviations in your data documentation.
 
 Try to think about the different statuses (raw data, draft, temporary, finished, ...) and types (editable source format like textfiles or csv in contrast to export formats such as PDF) of your documents.
 
@@ -100,7 +100,7 @@ Different disciplines use established standards, see [Data Formats](/docs/data_f
 
 ## Folder structure
 
-Everything should be immediately intuitively understandable. The folder structure helps you to navigate through the individual pieces of information. Develop a convention when the project starts and write it down including explaination of abbreviations in your [data documentation](/docs/data_documentation). Try to consistently apply the same strategy in every project within the research group.
+Everything should be immediately intuitively understandable. The folder structure helps you to navigate through the individual pieces of information. Develop a convention when the project starts and write it down including explaination of abbreviations in your data documentation. Try to consistently apply the same strategy in every project within the research group.
 
 Folders should:
 

docs/60_topics/63_data_description_annotation/00_data_description_annotation.mdx → docs/30_data/35_data_annotation/35_data_description_annotation.mdx

@@ -4,6 +4,17 @@ slug: "/data_description_annotation"
 id: "data_description_annotation"
 ---
 
+import FloatImage from "@site/src/components/commons/FloatImage";
+
+# Data Description and Annotation
+
+<FloatImage
+	url="/img/data/dataarcheo.jpg"
+	alt="Data Description and Annotation"
+	width="min(100%, 300px)"
+	caption="no title, CC-BY, by Auke Herrema"
+/>
+
 To best understand a dataset at hand, whether it be for yourself, your working group, or others in the scientific community, it must be clearly described. To this end, data annotation and data description are both important aspects of data management and analysis and enable reuse of research data.
 
 Data description involves providing a detailed overview of the data, including its characteristics, format, structure, and any relevant context. Research data annotation is the process of adding labels or notes to data to make it more easily understandable and accessible for analysis. In using rich machine-readable [metadata](/docs/metadata), data annotation and description allows both humans and machines to make informed decisions about the suitability of the data for their research.