diff --git a/docs/00_intro/10_fair.mdx b/docs/00_intro/10_fair.mdx index 039d4f7b..2836c690 100644 --- a/docs/00_intro/10_fair.mdx +++ b/docs/00_intro/10_fair.mdx @@ -130,7 +130,7 @@ In simple terms: metadata include any relevant history. If the dataset is relate #### R1.3. (meta)data meet domain-relevant community standards {#r1_3} -As research data management and, as such, [data publishing](/docs/data_publishing) becomes more and more prevalent across research areas, [best practices](/docs/best_practice) in the individual communities will arise. This should encompass metadata templates for proper documentation of datasets, how the data should be [organized](/docs/data_organisation), which vocabularies or [ontologies](/docs/ontology) to use, and [file formats](/docs/format_standards). NFDI4Chem is working to establish [metadata and data standards](https://nfdi4chem.de/your-nfdi4chem-team-get-to-know-the-consortium-4/) for the various communities in chemistry. +As research data management and, as such, [data publishing](/docs/data_publishing) becomes more and more prevalent across research areas, [best practices](/docs/best_practice) in the individual communities will arise. This should encompass metadata templates for proper documentation of datasets, how the data should be [organized](/docs/data_organisation), which vocabularies or [ontologies](/docs/ontology) to use, and [file formats](/docs/data_formats). NFDI4Chem is working to establish [metadata and data standards](https://nfdi4chem.de/your-nfdi4chem-team-get-to-know-the-consortium-4/) for the various communities in chemistry. Where available, community standards and best practices should be followed when those publishing prepare their datasets and relevant metadata for publication. [Repositories](/docs/repositories), especially domain-specific service providers, should adhere to the standards set forth by the community by requiring files and metadata to follow format specifications. diff --git a/docs/00_intro/20_data_life_cycle.mdx b/docs/00_intro/20_data_life_cycle.mdx index dc2d574f..0ad28d55 100644 --- a/docs/00_intro/20_data_life_cycle.mdx +++ b/docs/00_intro/20_data_life_cycle.mdx @@ -21,7 +21,7 @@ To ensure compliance, professional handling of research data is of particular im ## Phase 1: Experiment Design -In phase 1, you start with the **planning of your experiment design** as well as planning your **data management** ([formats](/docs/format_standards), [storage](/docs/data_storage) …). During this planning phase, already **existing data** should be located and the **legal framework** for use should be checked. +In phase 1, you start with the **planning of your experiment design** as well as planning your **data management** ([formats](/docs/data_formats), [storage](/docs/data_storage) …). During this planning phase, already **existing data** should be located and the **legal framework** for use should be checked. You should be aware of the various **requirements** of the research funder, your university, your institution, and your community and should write a **first draft** of your [data management plan (DMP)](/docs/dmp). In this first draft, you should define the **responsibilities**. ## Phase 2: Experiment/ Data Collection @@ -45,7 +45,7 @@ Before sharing data, you should check whether the data is subject to **copyright During this exchange and the associated reflections on the data, you should think about archiving and using the data in scientific publications. If you are not aware of any **criteria for archiving** and no criteria are specified in your working group or institute, decision-making guides such as the [“5 steps to decide what data to keep”](https://www.dcc.ac.uk/guidance/how-guides/five-steps-decide-what-data-keep) outlined by the DCC can help. Based on the established criteria, it is determined which of the collected raw data should be archived and which should be deliberately deleted. -In addition to the criteria, the migration of the data into **suitable [formats](/docs/format_standards) and onto suitable media** is important for archiving the data. In this step, the data should again be enriched with metadata so that it can be understood in the future without further knowledge about the data. +In addition to the criteria, the migration of the data into **suitable [formats](/docs/data_formats) and onto suitable media** is important for archiving the data. In this step, the data should again be enriched with metadata so that it can be understood in the future without further knowledge about the data. In addition to archiving, the [publication](/docs/data_publishing) of the data plays a special role. Many research funders expect the data to be published if there are no special reasons not to do so, such as a non-disclosure agreement or the inclusion of personal data. A **chemistry-specific or chemistry-related [repository](/docs/repositories)** such as the [Chemotion Repository](https://www.chemotion-repository.net/), [NOMAD](https://nomad-lab.eu/services/repo-arch), or [MassBank](https://massbank.eu/MassBank/) is recommended for the publication of data. An overview of repositories can be found, for example, at [re3data.org](https://www.re3data.org/) or [fairsharing.org](https://fairsharing.org/). re3data.org allows you to filter repositories according to certain criteria such as the assignment of a persistent identifier or access. Data publishing often takes place at certain milestones, for example, in combination with a text publication or at the end of a project. The **final version of the data management plan** is also required at the end of a project. diff --git a/docs/10_domains/60_synthetic_chemistry.mdx b/docs/10_domains/60_synthetic_chemistry.mdx index f0d04de3..efe5be9f 100644 --- a/docs/10_domains/60_synthetic_chemistry.mdx +++ b/docs/10_domains/60_synthetic_chemistry.mdx @@ -19,7 +19,7 @@ In synthesising a compound, every step of an experiment, from planning and execu Synthetic chemistry generates diverse data beyond product characterisation. A typical experiment starts with design and planning, then proceeds in the lab while observations, conditions, and yields are documented. Ideally, an [electronic lab notebook (ELN)](/docs/eln) is used to collect and structure this data. -After synthesis, product properties are analysed using both manual and digital data. Results from methods without digital output (e.g., melting/boiling point, optical rotation, TLC Rf, refractive index) can be entered manually in the ELN, while digital instrument data (e.g., NMR, IR, MS) can be uploaded and analysed there. Raw files should be preserved in [proprietary file formats](/docs/format_standards) and, where possible, converted to interoperable [open file formats](/docs/format_standards); if no open standard exists, export to `.txt` or `.csv` is recommended. +After synthesis, product properties are analysed using both manual and digital data. Results from methods without digital output (e.g., melting/boiling point, optical rotation, TLC Rf, refractive index) can be entered manually in the ELN, while digital instrument data (e.g., NMR, IR, MS) can be uploaded and analysed there. Raw files should be preserved in [proprietary file formats](/docs/data_formats) and, where possible, converted to interoperable [open file formats](/docs/data_formats); if no open standard exists, export to `.txt` or `.csv` is recommended. Overall, [metadata](/docs/metadata) should always be included when collecting and [storing](/docs/data_storage) data to allow understanding of the research data in the long term. @@ -27,7 +27,7 @@ Overall, [metadata](/docs/metadata) should always be included when collecting an 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. -Applying [FAIR principles](/docs/fair) retrospectively to analogue workflows is time-consuming. ELNs help by automating key FAIR-related tasks, such as structured [metadata](/docs/metadata) capture in human- and [machine-readable formats](/docs/format_standards), and in some cases generation of interoperable [open file formats](/docs/format_standards/#chemistry_formats). Because ELN selection is critical, consult [how to choose the right ELN](/docs/choose_eln): +Applying [FAIR principles](/docs/fair) retrospectively to analogue workflows is time-consuming. ELNs help by automating key FAIR-related tasks, such as structured [metadata](/docs/metadata) capture in human- and [machine-readable formats](/docs/data_formats), and in some cases generation of interoperable [open file formats](/docs/data_formats). Because ELN selection is critical, consult [how to choose the right ELN](/docs/choose_eln): diff --git a/docs/20_role/30_student.mdx b/docs/20_role/30_student.mdx index 000ec344..a876c321 100644 --- a/docs/20_role/30_student.mdx +++ b/docs/20_role/30_student.mdx @@ -7,7 +7,6 @@ slug: "/student" This article applies to students, who are about to start their bachelor or master thesis. ::: - ## Data production :::info How: @@ -29,10 +28,9 @@ For the [documentation of these data](/docs/data_documentation), a classic paper ::: :::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/format_standards), which is suitable for [publication](/docs/data_publishing) in the experimental section of a thesis or a paper.
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](/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.
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 :::danger Why: @@ -47,7 +45,6 @@ Data can be processed by different software for each method. But an [ELN](/docs/ 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). ::: - ## Sharing, preserving and re-using data :::danger Why: @@ -55,7 +52,7 @@ Collaboration is easier if [well-organised data](/docs/data_organisation) is acc ::: :::info How: -An [ELN](/docs/eln) represents a powerful internal database. Within [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) your own results can be searched by structure or keyword. Furthermore, scientific search engines like SciFinder can be implemented into an [ELN](/docs/eln). +An [ELN](/docs/eln) represents a powerful internal database. Within [Chemotion](https://www.chemotion.net/chemotionsaurus/index.html) your own results can be searched by structure or keyword. Furthermore, scientific search engines like SciFinder can be implemented into an [ELN](/docs/eln). ::: :::note Note: diff --git a/docs/20_role/40_data_steward.mdx b/docs/20_role/40_data_steward.mdx index 8113e334..27c768e0 100644 --- a/docs/20_role/40_data_steward.mdx +++ b/docs/20_role/40_data_steward.mdx @@ -11,7 +11,7 @@ From this definition, it follows that a data steward is concerned with, in broad ## Tasks and Skills -A data steward's task can take on various forms, often depending on the exact position within the organization and the individual's personal skill set and background. The duties may involve defining appropriate [repositories](/docs/repositories) for [data publishing](/docs/data_publishing), consulting on [data formats](/docs/format_standards) and [metadata](/docs/metadata) standards, setting up data documentation and metadata management systems, e.g. through an [electronic laboratory notebook (ELN)](/docs/eln), and training researchers on [best practices](/docs/best_practice). The [Engaging Researchers with Data Management: The Cookbook](https://www.openbookpublishers.com/product/1080) elaborates on the various tasks and skills for the role of data steward. In it, Alastair Dunning, the Head of +A data steward's task can take on various forms, often depending on the exact position within the organization and the individual's personal skill set and background. The duties may involve defining appropriate [repositories](/docs/repositories) for [data publishing](/docs/data_publishing), consulting on [data formats](/docs/data_formats) and [metadata](/docs/metadata) standards, setting up data documentation and metadata management systems, e.g. through an [electronic laboratory notebook (ELN)](/docs/eln), and training researchers on [best practices](/docs/best_practice). The [Engaging Researchers with Data Management: The Cookbook](https://www.openbookpublishers.com/product/1080) elaborates on the various tasks and skills for the role of data steward. In it, Alastair Dunning, the Head of Research Data Services at TU Delft, is quoted saying: > “Interpersonal skills are key to the success of the Data Steward.” diff --git a/docs/30_data/00_data_guide.mdx b/docs/30_data/00_data_guide.mdx index ef92e5ee..d3240121 100644 --- a/docs/30_data/00_data_guide.mdx +++ b/docs/30_data/00_data_guide.mdx @@ -7,7 +7,7 @@ id: "data_guide" import FeatureButton from "@site/src/components/features/FeatureButton"; :::info Guidance for getting started: -In this section, you can find information and resources about important topics and how to handle your data. These overarching concepts will provide key information and recommendations (such as what software might be useful to you) and refer you to other appropriate pages within the knowledge base or external resources. +In this section, you can find information and resources about important topics and how to handle your data. These overarching concepts will provide key information and recommendations (such as what software might be useful to you) and refer you to other appropriate pages within the knowledge base. ::: ### Data Management Plan diff --git a/docs/30_data/30_data_organisation.mdx b/docs/30_data/30_data_organisation.mdx index 9aa22c08..043c340e 100644 --- a/docs/30_data/30_data_organisation.mdx +++ b/docs/30_data/30_data_organisation.mdx @@ -27,41 +27,41 @@ Find a balanced set of elements: Too many make it difficult to grasp quickly, wh :::note General basics for naming files: -- Order the elements from general to specific. -- Use meaningful abbreviations instead of long identifiers. -- Use underscore `_`, hyphen `-` or capitalized letters to separate elements in the name. Don’t use spaces or special characters: `?!&,_%#;_()@$^~‘{}[]<>`. -- Use date format ISO8601: `YYYYMMDD`, and time if needed `HHMMSS`. -- Include a version number if appropriate: minimum two digits (V02) and extend it, if needed for minor corrections (V02-03). The leading zeros, will ensure the files are sorted correctly. +- Order the elements from general to specific. +- Use meaningful abbreviations instead of long identifiers. +- Use underscore `_`, hyphen `-` or capitalized letters to separate elements in the name. Don’t use spaces or special characters: `?!&,_%#;_()@$^~‘{}[]<>`. +- Use date format ISO8601: `YYYYMMDD`, and time if needed `HHMMSS`. +- Include a version number if appropriate: minimum two digits (V02) and extend it, if needed for minor corrections (V02-03). The leading zeros, will ensure the files are sorted correctly. (by [RDMKit](https://rdmkit.elixir-europe.org/data_organisation.html#what-is-the-best-way-to-name-a-file)) ::: #### Example elements to include in the file name -- Date of creation -- Project number / experiment / acronym -- Type of data (sample ID, analysis, conditions, modifications etc.) -- Device / location / coordinates -- Name / Initials of the creator -- Version number -- Reserve the last 3-letters for file format (e.g. .csv, .odt, .tif, .jpg) +- Date of creation +- Project number / experiment / acronym +- Type of data (sample ID, analysis, conditions, modifications etc.) +- Device / location / coordinates +- Name / Initials of the creator +- Version number +- Reserve the last 3-letters for file format (e.g. .csv, .odt, .tif, .jpg) A good file name such as `20180211_ELI5_TEMP_BH01_RAW_03.csv` can easily be sorted by date and tells you: -- **date of file:** 11 February 2018 -- **project acronym:** ELI5 = Explain like I'm 5 (years old) -- **measured value type:** TEMP = temperature values -- **measuring point / location:** BH01 = beehive # 01 -- **type of data:** RAW = raw data from measuring device -- **number of file:** containing data for that measurement series +- **date of file:** 11 February 2018 +- **project acronym:** ELI5 = Explain like I'm 5 (years old) +- **measured value type:** TEMP = temperature values +- **measuring point / location:** BH01 = beehive # 01 +- **type of data:** RAW = raw data from measuring device +- **number of file:** containing data for that measurement series If you need to rename a multiple files, take a look at: -- [Thunar Bulk Rename](https://docs.xfce.org/xfce/thunar/bulk-renamer/start) (Linux, GUI) -- [command line: mv, mmv, rename](https://linuxconfig.org/how-to-rename-multiple-files-on-linux) (Linux, CLI) -- [Bulk Rename Utility](https://www.bulkrenameutility.co.uk/) (Windows, free) -- [TotalCommander](https://www.ghisler.com/advanced.htm#tutorial_rename) (windows, Shareware) -- [Renamer4Mac](https://renamer.com/) (Mac). +- [Thunar Bulk Rename](https://docs.xfce.org/xfce/thunar/bulk-renamer/start) (Linux, GUI) +- [command line: mv, mmv, rename](https://linuxconfig.org/how-to-rename-multiple-files-on-linux) (Linux, CLI) +- [Bulk Rename Utility](https://www.bulkrenameutility.co.uk/) (Windows, free) +- [TotalCommander](https://www.ghisler.com/advanced.htm#tutorial_rename) (windows, Shareware) +- [Renamer4Mac](https://renamer.com/) (Mac). For some special file formats there are tools for adapting the file name to metadata. For example, to create a file name that fits your scheme and takes date and time information from the EXIF data of a jpg file. Some also allow adding an offset - this helps sort photos into timestamps that run on different clocks. @@ -71,31 +71,31 @@ Stash snapshots or simply track changes and allow to find something that existed #### Possible Solutions -- Low number of requirements: manage manually e.g. by keeping a log where the changes for each respective file is documented, version by version. -- For automatic management of versioning, conflict resolution and back-tracing capabilities, use a proper version control software such as [Git](https://git-scm.com/), hosted by e.g. [GitHub](https://github.com/) or your home institution. Very strong with uncompressed, readable and comparable files such as text files or csv. -- Use a Cloud Storage service (see [Data Storage and Archiving](/docs/data_storage)) that provides automatic file versioning. Very strong on spreadsheets, text files and slides. +- Low number of requirements: manage manually e.g. by keeping a log where the changes for each respective file is documented, version by version. +- For automatic management of versioning, conflict resolution and back-tracing capabilities, use a proper version control software such as [Git](https://git-scm.com/), hosted by e.g. [GitHub](https://github.com/) or your home institution. Very strong with uncompressed, readable and comparable files such as text files or csv. +- Use a Cloud Storage service (see [Data Storage and Archiving](/docs/data_storage)) that provides automatic file versioning. Very strong on spreadsheets, text files and slides. ## Files: types of metadata Consider the way data and [Metadata](/docs/metadata/) can be stored together as FDOs (Fair Digital Objects). For example, metadata can be divided into the following four categories: -- descriptive metadata -- administrative metadata -- technical metadata -- structural metadata +- descriptive metadata +- administrative metadata +- technical metadata +- structural metadata -An FDO encapsulates data and metadata in one file and can be saved as an [HDF5](https://www.hdfgroup.org/solutions/hdf5/), for example. See [Data Format Standard](/docs/format_standards/) for more information. +An FDO encapsulates data and metadata in one file and can be saved as an [HDF5](https://www.hdfgroup.org/solutions/hdf5/), for example. See [Data Format Standard](/docs/data_formats/) for more information. ## Files: formats -Different disciplines use established standards, see [Data Format Standard](/docs/format_standards/). Also consider beyond the duration of the project: +Different disciplines use established standards, see [Data Format Standard](/docs/data_formats/). Also consider beyond the duration of the project: -- usage of proprietary or open file formats -- exchange within and outside of the working group -- short term and long term storage, [Data Storage and Archiving](/docs/data_storage) -- special workflows and procedures (e.g. for data collection, documentation and evaluation) -- shared or separate storage (e.g. SPSS file for data; XML file for metadata) -- suitable procedures for preserving the consistency of data and metadata +- usage of proprietary or open file formats +- exchange within and outside of the working group +- short term and long term storage, [Data Storage and Archiving](/docs/data_storage) +- special workflows and procedures (e.g. for data collection, documentation and evaluation) +- shared or separate storage (e.g. SPSS file for data; XML file for metadata) +- suitable procedures for preserving the consistency of data and metadata ## Folder structure @@ -103,9 +103,9 @@ Everything should be immediately intuitively understandable. The folder structur Folders should: -- follow a structure with folders and subfolders that correspond to the project design and workflow -- have a self-explanatory name that is only as long as is necessary -- have a unique name – avoid assigning the same name to a folder and a subfolder +- follow a structure with folders and subfolders that correspond to the project design and workflow +- have a self-explanatory name that is only as long as is necessary +- have a unique name – avoid assigning the same name to a folder and a subfolder The top folder should have a README.txt file describing the folder structure and what files are contained within the folders. This file should also contain explanation of the file naming convention. @@ -130,6 +130,6 @@ The top folder should have a README.txt file describing the folder structure and ## Sources and further information -- https://rdmkit.elixir-europe.org/data_organisation.html -- https://www.data.cam.ac.uk/data-management-guide/organising-your-data -- https://libguides.vu.nl/rdm/data-documentation +- https://rdmkit.elixir-europe.org/data_organisation.html +- https://www.data.cam.ac.uk/data-management-guide/organising-your-data +- https://libguides.vu.nl/rdm/data-documentation diff --git a/docs/30_data/40_data_documentation.mdx b/docs/30_data/40_data_documentation.mdx index 389d745d..12a8cc08 100644 --- a/docs/30_data/40_data_documentation.mdx +++ b/docs/30_data/40_data_documentation.mdx @@ -49,7 +49,7 @@ The result should be descriptive documents that unambiguously define for files w ### Technical implementations -Get an overview of the occurring data and document flows. A short description, easy to understand for every user, should be accessible on a low level and explain the main concepts. The data itself needs to have a bulletproof [Data Organisation](/docs/data_organisation) including [Metadata](/docs/metadata/) and suitable [Data Format Standards](/docs/format_standards/). Find out more about the usual [Best Practice](/docs/best_practice/) at your institute or within your discipline. Plan how documents can be shared between project staff and what needs to be accessed for [Data Publication](/docs/data_publishing/) with [PID](/docs/pid/). +Get an overview of the occurring data and document flows. A short description, easy to understand for every user, should be accessible on a low level and explain the main concepts. The data itself needs to have a bulletproof [Data Organisation](/docs/data_organisation) including [Metadata](/docs/metadata/) and suitable [Data Format Standards](/docs/data_formats/). Find out more about the usual [Best Practice](/docs/best_practice/) at your institute or within your discipline. Plan how documents can be shared between project staff and what needs to be accessed for [Data Publication](/docs/data_publishing/) with [PID](/docs/pid/). Data security affects all technical and organisational issues to protect the data from alteration, loss, and destruction. In this context, storage methods, backup procedures, necessary physical resources as well as automated and administrative routines must be planned and put in place. Ask local contacts or external experts about already established technologies for [Data Storage and Archiving](/docs/data_storage) as well as suitable [Repositories](/docs/repositories/). diff --git a/docs/40_smartlab/00_smartlab.mdx b/docs/40_smartlab/00_smartlab.mdx index 6178eefc..a564d31b 100644 --- a/docs/40_smartlab/00_smartlab.mdx +++ b/docs/40_smartlab/00_smartlab.mdx @@ -7,13 +7,21 @@ id: "smartlab" import FeatureButton from "@site/src/components/features/FeatureButton"; import useBaseUrl from "@docusaurus/useBaseUrl"; -# Smart Laboratory (Smart Lab) +# Smart Laboratory (SmartLab) ![smartlab_flow](/img/smartlab/smartlab_flow2.png) -A smart lab represents a holistic approach to [data management](/docs/data_guide) in chemistry with seamless data flows. What does this mean? It means that all steps within a researcher's [workflow](/docs/domain_guide) across the [research data lifecycle](/docs/data_life_cycle) are interconnected in a digital way. The key difference to a [Laboratory Management System (LIMS)](https://en.wikipedia.org/wiki/Laboratory_information_management_system) is that the Smart Lab's main focus is the realisation of the [FAIR data principles](/docs/fair). For example, a researcher plans and [documents](/docs/data_documentation) their experiment in an [electronic lab notebook (ELN)](/docs/eln). Any experimental data from devices such as spectrometers are then directly ingested by the ELN via [Application Programming Interfaces (APIs)](https://en.wikipedia.org/wiki/API). +**SmartLab – a holistic, FAIR‑centric approach to research data management in chemistry** -The ELN then ideally assigns all the necessary [metadata](/docs/metadata) automatically and appropriately for a corresponding workflow and converts proprietary [data formats](/docs/format_standards) to open data formats. The ELN structures the (meta)data and experimental descriptions in a meaningful and sustainable way which is both human- and machine-readable (e.g., via the use of [machine-readable chemical structures](/docs/machine-readable_chemical_structures)). When the researcher chooses to [publish](/docs/data_publishing) or [archive](/docs/data_storage) their data, it is then ingested seamlessly by a data [repository](/docs/repositories) or archive without much further work as the ELN has already appropriately prepared the dataset to meet a repository’s or archive’s [requirements](/docs/choose_repository). +A **SmartLab** integrates every step of a chemist’s workflow — from planning to archiving — into a seamless digital ecosystem. Unlike a traditional Laboratory Management System (LIMS), the primary goal of a SmartLab is to realise the [FAIR data principles](/docs/fair) (Findable, Accessible, Interoperable, Re‑usable). + +1. **Planning & documentation** – The researcher records the experimental design in an [electronic lab notebook (ELN)](/docs/eln). +2. **Data acquisition** – Instruments (e.g., spectrometers) push raw data directly into the ELN via an [Application Programming Interface (API)](https://en.wikipedia.org/wiki/API). +3. **Automatic metadata & format conversion** – The ELN assigns the appropriate [metadata](/docs/metadata) and converts proprietary data formats into open, [standards‑compliant formats](/docs/data_formats). +4. **Structured, machine‑readable records** – Both the (meta)data and the experimental description are organised in a way that is readable by humans and machines, e.g., using [machine‑readable chemical structures](/docs/machine-readable_chemical_structures). +5. **Publishing & archiving** – When the researcher decides to publish or archive the work, the ELN already supplies a FAIR‑ready package that can be ingested by a [repository](/docs/repositories) or **archive** with minimal additional effort. + +In short, a SmartLab turns the entire [research data lifecycle](/docs/data_life_cycle) into a coherent, automated process, freeing scientists to focus on discovery rather than data wrangling. In this section, key components of the smart lab will be introduced to you. diff --git a/docs/40_smartlab/10_eln.mdx b/docs/40_smartlab/10_eln.mdx index d5688f23..42752368 100644 --- a/docs/40_smartlab/10_eln.mdx +++ b/docs/40_smartlab/10_eln.mdx @@ -57,13 +57,16 @@ One of the most important things that distinguishes an ELN from a blank piece of ## Advantages of an ELN -ELNs help to link experimental descriptions directly to the collected data so that all information can be found in one place. Furthermore, data loss is avoided by secure data storage and backups. Storing all the data in one central place helps also with knowledge management because the data is easily findable and accessible, even for new members in a research project. The biggest advantage of an ELN is that [metadata](/docs/metadata) is stored in a structured and [standardised](/docs/format_standards) way. This also helps with publishing research results and transferring research data to a repository. +ELNs help to link experimental descriptions directly to the collected data so that all information can be found in one place. Furthermore, data loss is avoided by secure data storage and backups. Storing all the data in one central place helps also with knowledge management because the data is easily findable and accessible, even for new members in a research project. The biggest advantage of an ELN is that [metadata](/docs/metadata) is stored in a structured and [standardised](/docs/data_formats) way. This also helps with publishing research results and transferring research data to a repository.

Avoid Data Loss

    -
  • Linking experimental descriptions to collected data (analogue and digital)
    • +
  • + Linking experimental descriptions to collected data (analogue and + digital) +
  • Secure data storage, backups
 diff --git a/docs/40_smartlab/15_choose_eln.mdx b/docs/40_smartlab/15_choose_eln.mdx index 1ba4de6c..4f3679ca 100644 --- a/docs/40_smartlab/15_choose_eln.mdx +++ b/docs/40_smartlab/15_choose_eln.mdx @@ -6,7 +6,7 @@ id: "choose_eln" import useBaseUrl from "@docusaurus/useBaseUrl"; -To date, there is not one ELN, that fits the requirements for all chemical disciplines. Furthermore, only a few ELNs meet the basic requirements for chemical sciences. This is most likely due to the challenges arising from drawing and processing chemical structures, a crucial and central step in being able to correlate research data to the corresponding chemical reaction or structure. The organisation of work with ELNs that support chemistry-specific functions can offer several benefits, such as the availability of [chemical structure identifiers](/docs/machine-readable_chemical_structures) and [standardised formats](/docs/format_standards) as well as the integration of manifold tools and workflows that facilitate scientific work. The direct integration of tools enables a direct support of the scientist without the need to search for additional services and software, thus bringing new developments and information directly to the scientists’ awareness. Some Open Source examples for systems in chemistry that offer the necessary support for chemical structures include [Indigo-ELN](https://lifescience.opensource.epam.com/indigo/eln/index.html), [LabTrove](https://sourceforge.net/projects/labtrove/), [OpenEnventory](http://www.open-enventory.de/), and [Chemotion ELN](https://chemotion.net/docs/eln). +To date, there is not one ELN, that fits the requirements for all chemical disciplines. Furthermore, only a few ELNs meet the basic requirements for chemical sciences. This is most likely due to the challenges arising from drawing and processing chemical structures, a crucial and central step in being able to correlate research data to the corresponding chemical reaction or structure. The organisation of work with ELNs that support chemistry-specific functions can offer several benefits, such as the availability of [chemical structure identifiers](/docs/machine-readable_chemical_structures) and [standardised formats](/docs/data_formats) as well as the integration of manifold tools and workflows that facilitate scientific work. The direct integration of tools enables a direct support of the scientist without the need to search for additional services and software, thus bringing new developments and information directly to the scientists’ awareness. Some Open Source examples for systems in chemistry that offer the necessary support for chemical structures include [Indigo-ELN](https://lifescience.opensource.epam.com/indigo/eln/index.html), [LabTrove](https://sourceforge.net/projects/labtrove/), [OpenEnventory](http://www.open-enventory.de/), and [Chemotion ELN](https://chemotion.net/docs/eln). Therefore, choosing the right ELN is crucial for a successful implementation of an ELN in a laboratory. Three steps should be taken to establish an ELN: 1) Needs assessment, 2) Testing and 3) Introducing the chosen ELN. diff --git a/docs/50_data_publication/00_data_publishing.mdx b/docs/50_data_publication/00_data_publishing.mdx index a8827f2e..5e438a11 100644 --- a/docs/50_data_publication/00_data_publishing.mdx +++ b/docs/50_data_publication/00_data_publishing.mdx @@ -12,7 +12,7 @@ This page applies to all researchers who want to publish their data. In chemical research, we strive to share results with others, commonly through articles in renowned scientific journals. To be able to actually work with and build upon these results, the scientific community also requires the data that the results were based on. -Publishing and therefore sharing these chemistry research data in a [FAIR](/docs/fair) manner by considering aspects such as [persistent identifiers](/docs/pid), rich [metadata](/docs/metadata), [provenance information](/docs/provenance), [data formats standards](/docs/format_standards) for analytical data, information on the [license](/docs/licences) applied, and by providing [machine-readable chemical structures](/docs/machine-readable_chemical_structures) adds value to the research results and enables discovery and reuse. +Publishing and therefore sharing these chemistry research data in a [FAIR](/docs/fair) manner by considering aspects such as [persistent identifiers](/docs/pid), rich [metadata](/docs/metadata), provenance information, [data formats standards](/docs/data_formats) for analytical data, information on the [license](/docs/licences) applied, and by providing [machine-readable chemical structures](/docs/machine-readable_chemical_structures) adds value to the research results and enables discovery and reuse. To publish data is essential to ensure that findings are transparent and reproducible. Moreover, it prevents duplicate efforts to generate data, hence, data publishing is also a measure of sustainability. diff --git a/docs/50_data_publication/10_repositories.mdx b/docs/50_data_publication/10_repositories.mdx index 331b0e67..cec4857a 100644 --- a/docs/50_data_publication/10_repositories.mdx +++ b/docs/50_data_publication/10_repositories.mdx @@ -12,8 +12,8 @@ Research data repositories are locations where digital objects are stored and ma Repositories can be classified mainly according to: -- the type of objects to be stored (e.g. scientific articles or research data) and -- the domain of the data contained (field-specific or generic repositories), +- the type of objects to be stored (e.g. scientific articles or research data) and +- the domain of the data contained (field-specific or generic repositories), Repositories can be hosted at institutional servers or are provided by broader organisations or consortia such as NFDI4Chem. The use of repositories is essential for data deposition according to the [FAIR Data Principles](/docs/fair). @@ -23,7 +23,7 @@ A repository is constituted by a repository software and a database. Researchers Some, but not all, repositories curate and review the data before **ingestion** with regard to their content and quality, sometimes also regarding legal aspects (copyright, data protection, [licences](/docs/licences)). -In order to allow data reuse by other researchers, [metadata](/docs/metadata), including [provenance information](/docs/provenance/), are required beside the actual data. Metadata describe the research data and provide information about its creation, the methods or software used as well as legal aspects. Metadata can be either added manually via a metadata editor or can be provided through other applications. The process to manually add metadata via a metadata editor can be compared to the process of submitting a manuscript to a publisher via the publishers submission system. +In order to allow data reuse by other researchers, [metadata](/docs/metadata), including provenance information, are required beside the actual data. Metadata describe the research data and provide information about its creation, the methods or software used as well as legal aspects. Metadata can be either added manually via a metadata editor or can be provided through other applications. The process to manually add metadata via a metadata editor can be compared to the process of submitting a manuscript to a publisher via the publishers submission system. One main function of repositories is to provide a search function, with which users and machines can find, view, and download data. In order to ensure that data are permanently referenced and can be [linked and cited](/docs/best_practice/), repositories assign unique [persistent identifiers](/docs/pid) (PIDs). This also enhances the findability and accessibility of research data. @@ -41,11 +41,11 @@ To ease the selection of a suitable research data repository for chemistry resea ## Sources and further information -- [Repository Platforms for Research Data IG](https://www.rd-alliance.org/groups/repository-platforms-research-data/activity/) -- [FAIRsFAIR Repository Support Series: Using registries to improve the visibility of your repository service](https://www.dcc.ac.uk/events/fairsfair-repository-support-series-using-registries-improve-visibility-your-repository) -- [The Repository Chemotion: infrastructure for sustainable research in chemistry](https://doi.org/10.1002/anie.202007702) -- [Chemotion ELN: an open source electronic lab notebook for chemists in academia](https://doi.org/10.1186/s13321-017-0240-0) -- German: [Was ist ein Repositorium? Forschungsdaten.info](https://www.forschungsdaten.info/themen/veroeffentlichen-und-archivieren/repositorien/) +- [Repository Platforms for Research Data IG](https://www.rd-alliance.org/groups/repository-platforms-research-data/activity/) +- [FAIRsFAIR Repository Support Series: Using registries to improve the visibility of your repository service](https://www.dcc.ac.uk/events/fairsfair-repository-support-series-using-registries-improve-visibility-your-repository) +- [The Repository Chemotion: infrastructure for sustainable research in chemistry](https://doi.org/10.1002/anie.202007702) +- [Chemotion ELN: an open source electronic lab notebook for chemists in academia](https://doi.org/10.1186/s13321-017-0240-0) +- German: [Was ist ein Repositorium? Forschungsdaten.info](https://www.forschungsdaten.info/themen/veroeffentlichen-und-archivieren/repositorien/) _This page is licensed under a Creative Commons Universal ([CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/deed.en)) Public Domain Dedication International License._ diff --git a/docs/50_data_publication/25_best_practice.mdx b/docs/50_data_publication/25_best_practice.mdx index f2617c53..194cfd7c 100644 --- a/docs/50_data_publication/25_best_practice.mdx +++ b/docs/50_data_publication/25_best_practice.mdx @@ -15,7 +15,7 @@ For the field-specific repositories [Chemotion Repository](/docs/chemotion_repos Other field-specific repositories such as [CSD and ICSD](/docs/csd_icsd), [Strenda DB](/docs/strenda_db), [SupraBank](/docs/suprabank), [NOMAD](/docs/nomad), and [Chemotion Repository](/docs/chemotion_repository), if used independently from Chemotion ELN, offer built-in workflows for the aggregation of structured data along the submission pathways, aligned with the [FAIR Data Principles](/docs/fair). This greatly facilitates the publication of FAIR data. -Further aspects to consider are to provide rich metadata, which could be [domain-independent](/docs/metadata/#domain-independent-metadata) metadata or [domain-specific](/docs/metadata/#domain-specific-metadata) metadata, and should also include [provenance information](/docs/provenance), which previously would have typically been included in the supplemental PDF section on general information and protocols. [Minimum information requirements](/docs/metadata/#minimum-information-standards-mi) should be followed, as set by the chemistry community, and implemented in articles and previously also in supplementary PDFs. +Further aspects to consider are to provide rich metadata, which could be [domain-independent](/docs/metadata/#domain-independent-metadata) metadata or [domain-specific](/docs/metadata/#domain-specific-metadata) metadata, and should also include provenance information, which previously would have typically been included in the supplemental PDF section on general information and protocols. [Minimum information requirements](/docs/metadata/#minimum-information-standards-mi) should be followed, as set by the chemistry community, and implemented in articles and previously also in supplementary PDFs. :::danger Notice: Are you asking yourself right now, "That's it?" Yes, that's it! As mentioned above, field specific repositories carry out much of the heavy lifting when it comes to FAIRification, while a digital smart lab environment even collects structured data directly along the scientific workflows and data life cycle. @@ -39,11 +39,11 @@ To start, all data to be published should be collected and ordered in a logical For further aspects to consider, we provide a non-exhaustive list of generic best practices on what a chemistry dataset should contain and how the chemical data should be represented and described. Please note, that [domain-specific](/docs/domain_guide) aspects also need to be considered. -- **Data should be published in open formats along the original raw data in proprietary formats.**
[Open formats](/docs/format_standards) for analytical data should be the main choice. However, many analytical instruments provide data in proprietary formats. Not all data in these formats is necessarily also included in the selected open format it was converted to, depending on the specification of the format applied. Hence, the original raw data (in proprietary formats) should also be published, although this data may have limited interoperability. Publishing original raw data is a measure of scientific integrity and allows for unbiased reprocessing and reuse of data. If no open format exists, export as text file, i.e. without any format specification, should be considered. -- **Data should be linked to chemical structures and reactions.**
As analytical data are usually named following lab journal entries, the dataset must include a description of which data correspond to which sample, molecule, or experiment. This should be included in the format used for analytical data. Another solution on that is to provide a [supplementary table](/docs/machine-readable_chemical_structures/#provide-machine-readable-data-as-supplementary-table) (figure 1) within the data package, which should include InChI structure identifiers and SMILES structure codes and additional information such as RInChI reaction identifiers. Second-tier, chemical structures might also be added and represented as [CT files](/docs/ct_files) such as mol or SDfiles (figure 1). [RXNfiles](/docs/ct_files) may be used to describe chemical reactions in a machine readable way. +- **Data should be published in open formats along the original raw data in proprietary formats.**
[Open formats](/docs/data_formats) for analytical data should be the main choice. However, many analytical instruments provide data in proprietary formats. Not all data in these formats is necessarily also included in the selected open format it was converted to, depending on the specification of the format applied. Hence, the original raw data (in proprietary formats) should also be published, although this data may have limited interoperability. Publishing original raw data is a measure of scientific integrity and allows for unbiased reprocessing and reuse of data. If no open format exists, export as text file, i.e. without any format specification, should be considered. +- **Data should be linked to chemical structures and reactions.**
As analytical data are usually named following lab journal entries, the dataset must include a description of which data correspond to which sample, molecule, or experiment. This should be included in the format used for analytical data. Another solution on that is to provide a [supplementary table](/docs/machine-readable_chemical_structures/#provide-machine-readable-data-as-supplementary-table) (figure 1) within the data package, which should include InChI structure identifiers and SMILES structure codes and additional information such as RInChI reaction identifiers. Second-tier, chemical structures might also be added and represented as [Chemical table files](https://en.wikipedia.org/wiki/Chemical_table_file) such as mol or SDfiles (figure 1). [RXNfiles](https://en.wikipedia.org/wiki/Chemical_table_file) may be used to describe chemical reactions in a machine readable way. - **Datasets should include scripts and workflows and information on software used.**
Workflows, i.e. scripts used for data processing, and input parameters for semi-automatic scripts, should be included. The metadata of the whole dataset should describe the language and version of scripts used as well as other software applied. Best practice is not only to include the code but to add notebooks such as R Notebooks or Jupyther notebooks. - **Data should be described with metadata and minimum information standards should be followed.**
[Domain-independent](/docs/metadata/#domain-independent-metadata) metadata, via the repository's metadata editor, should be provided. Additionally, [domain-specific](/docs/metadata/#domain-specific-metadata) metadata should be published via the metadata editor, if supported by the repository of choice, or within the analytical data or an additional README file. [Minimum information requirements](/docs/metadata/#minimum-information-standards-mi) as set by the chemistry community and followed in manuscripts and previously also in supplementary PDFs should be included. -- **The provenance information of data should be included.**
Part of that [provenance information](/docs/provenance) is part of the dataset's metadata and should be added via the metadata editor of the repository. All information which previously would have typically been included in the supplemental PDF section on general information and protocols, e.g. information on methods and instruments used, are provenance information and should be added to a README file, as long as no domain-specific metadata schema is available. This README could be a text file, or written in Markdown, while also a human-readable rendered representation as HTML could be provided. +- **The provenance information of data should be included.**
Part of that provenance information is part of the dataset's metadata and should be added via the metadata editor of the repository. All information which previously would have typically been included in the supplemental PDF section on general information and protocols, e.g. information on methods and instruments used, are provenance information and should be added to a README file, as long as no domain-specific metadata schema is available. This README could be a text file, or written in Markdown, while also a human-readable rendered representation as HTML could be provided. :::danger Notice: Does that sound like a lot of manual work? Avoid extra work by using a [smart lab](/docs/smartlab) digital environments for collecting, processing, analysing, and publishing research data, such as [Chemotion ELN](/docs/chemotion) in combination with [Chemotion Repository](/docs/chemotion_repository)! Plus, you may omit the preparation of supplementary PDFs and use the saved work time to prepare your dataset for publication! diff --git a/docs/50_data_publication/60_machine-readable_chemical_structures.mdx b/docs/50_data_publication/60_machine-readable_chemical_structures.mdx index df910553..d80152fc 100644 --- a/docs/50_data_publication/60_machine-readable_chemical_structures.mdx +++ b/docs/50_data_publication/60_machine-readable_chemical_structures.mdx @@ -9,7 +9,7 @@ import useBaseUrl from "@docusaurus/useBaseUrl"; Finding relevant articles based on IUPAC names or trivial names of molecules is a challenging task, while chemical identifiers allow for unambiguous identification of compounds. Redrawing of chemical structures is labour and time intensive, while chemical table (CT) files or SMILES structure codes can be used without any additional effort with any common structure drawing software. -Having machine-readable chemical structures as [CT files](/docs/ct_files), such as mol files, and as [SMILES](/docs/smiles) structure codes plus the [InChI](/docs/inchi) identifiers as part of a dataset associated with a research article will enhance its [findability](/docs/fair) by making the article easily indexable and structure searchable. This also improves [interoperability](/docs/fair) and facilitates [reuse](/docs/fair) of scientific work. +Having machine-readable chemical structures as [Chemical table files](https://en.wikipedia.org/wiki/Chemical_table_file), such as mol files, and as [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System) structure codes plus the [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier) identifiers as part of a dataset associated with a research article will enhance its [findability](/docs/fair) by making the article easily indexable and structure searchable. This also improves [interoperability](/docs/fair) and facilitates [reuse](/docs/fair) of scientific work. :::info Info: While such information is not required for structured field-specific repositories such as Chemotion Repository, as this information is provided by the repository software, datasets in generic repositories benefit from this information. @@ -18,7 +18,7 @@ While such information is not required for structured field-specific repositorie The following will provide a tutorial on how to increase the machine-readability of chemistry research articles by providing machine-readable chemical structures within the associated dataset. Recommendations to provide structure codes and identifiers in a machine-readable supplementary table within the associated dataset are given. :::danger Notice: -Please note that there are currently limitations with [SMILES](/docs/smiles) and [InChI](/docs/inchi) for inorganic and especially organometallic compounds. +Please note that there are currently limitations with [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System) and [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier) for inorganic and especially organometallic compounds. ::: ## Get mol files @@ -109,7 +109,7 @@ Additionally, this table may also include further columns for: Templates for such a table containing the minimum recommended columns are provided as [.ods](pathname://../../assets/Supplementary_table_template.ods), [.xlsx](pathname://../../assets/Supplementary_table_template.xlsx), and [.csv](pathname://../../assets/Supplementary_table_template.csv) files. These template files also take advantage of [ontologies](/docs/ontology) to unambiguously identify terms for humans and machines. :::danger Notice: -If the experimental work is documented in an [ELN](/docs/eln), these information could also be provided by the ELN system. Chemotion ELN generates [SMILES](/docs/smiles), [InChI](/docs/inchi), InChIKey as well as RInChI and RInChIKey for compounds and reactions. These information are also available in Chemotion Repository i.e. such a supplementary table is not required with structured, field-specific repositories such as Chemotion Repository, while datasets in generic repositories will profit from such a table. +If the experimental work is documented in an [ELN](/docs/eln), these information could also be provided by the ELN system. Chemotion ELN generates [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System), [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier), InChIKey as well as RInChI and RInChIKey for compounds and reactions. These information are also available in Chemotion Repository i.e. such a supplementary table is not required with structured, field-specific repositories such as Chemotion Repository, while datasets in generic repositories will profit from such a table. ::: _This page is licensed under a Creative Commons Universal ([CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/deed.en)) Public Domain Dedication International Licence._ diff --git a/docs/50_data_publication/70_publishing_standards/10_publishing_standards_authors.mdx b/docs/50_data_publication/70_publishing_standards/10_publishing_standards_authors.mdx index 768ff8af..dd61c952 100644 --- a/docs/50_data_publication/70_publishing_standards/10_publishing_standards_authors.mdx +++ b/docs/50_data_publication/70_publishing_standards/10_publishing_standards_authors.mdx @@ -13,7 +13,7 @@ This guide is based on our **standards for data publishing for authors**. You ca ## 1. Upload your Dataset -Once you have ensured your data is [well-organized](/docs/data_organisation) and you have chosen appropriate [data formats](/docs/format_standards), you can upload your files. Depending on the [repository you have chosen](/docs/choose_repository), it may be easiest to directly upload your data when creating the dataset. For example, [RADAR4Chem](https://radar4chem.radar-service.eu/) will automatically extract all files from a zip file at this point and replicate the directory structure, while this is not the case if the data is uploaded at a later point in time and an empty dataset is created first. +Once you have ensured your data is [well-organized](/docs/data_organisation) and you have chosen appropriate [data formats](/docs/data_formats), you can upload your files. Depending on the [repository you have chosen](/docs/choose_repository), it may be easiest to directly upload your data when creating the dataset. For example, [RADAR4Chem](https://radar4chem.radar-service.eu/) will automatically extract all files from a zip file at this point and replicate the directory structure, while this is not the case if the data is uploaded at a later point in time and an empty dataset is created first. Including a plain-text README file within your dataset can give more human-readable context to your data. You may also include additional metadata files if you feel the options offered by the repository are insufficient to give the full context to your dataset. @@ -29,8 +29,8 @@ These identifiers help improve the visibility of your work. When you deposit your research data, it will be assigned a [**persistent identifier (PID)**](/docs/pid), similarly to how your article is designated a DOI (Digital Object Identifier). In many cases, the dataset's PID will be a [DataCite](https://datacite.org/) DOI. Here’s how to use it: -- **For Your Own Datasets:** Include the PID in the [data availability statement](/docs/data_availability_statement) of your article, which informs readers where they can find your underlying data. Also, add this PID in the references section of your manuscript to cite it specifically within the text. -- **For Others’ Datasets:** If you reuse datasets published by other researchers, include their PIDs in your references instead of just citing their articles. This not only gives proper credit but also helps others locate those datasets easily. +- **For Your Own Datasets:** Include the PID in the [data availability statement](/docs/data_availability_statement) of your article, which informs readers where they can find your underlying data. Also, add this PID in the references section of your manuscript to cite it specifically within the text. +- **For Others’ Datasets:** If you reuse datasets published by other researchers, include their PIDs in your references instead of just citing their articles. This not only gives proper credit but also helps others locate those datasets easily. :::danger Notice: This distinction is important, because **the link** to the dataset in [Crossref](https://www.crossref.org/)'s DOI metadata for scientific articles **varies** depending on whether the dataset is a directly related source of information or a specifically referenced resource. @@ -40,8 +40,8 @@ This distinction is important, because **the link** to the dataset in [Crossref] To enhance discoverability, link your datasets directly to their corresponding articles: -- Add the article's DOI as a related identifier in the metadata of your dataset when submitting it to a repository. -- Use the relation type [`IsSupplementTo`](https://datacite-metadata-schema.readthedocs.io/en/4.5/appendices/appendix-1/relationType/#issupplementto) when linking datasets with articles, as recommended by [Crossref](https://www.crossref.org/documentation/schema-library/markup-guide-metadata-segments/relationships/) and [DataCite](https://datacite-metadata-schema.readthedocs.io/en/4.5/appendices/appendix-1/relationType/) guidelines. +- Add the article's DOI as a related identifier in the metadata of your dataset when submitting it to a repository. +- Use the relation type [`IsSupplementTo`](https://datacite-metadata-schema.readthedocs.io/en/4.5/appendices/appendix-1/relationType/#issupplementto) when linking datasets with articles, as recommended by [Crossref](https://www.crossref.org/documentation/schema-library/markup-guide-metadata-segments/relationships/) and [DataCite](https://datacite-metadata-schema.readthedocs.io/en/4.5/appendices/appendix-1/relationType/) guidelines. Many repositories will guide you here, offering dropdown menus to select the appropriate relation and identifier type or even automatically detect this information. @@ -49,8 +49,8 @@ Many repositories will guide you here, offering dropdown menus to select the app If you are working with multiple datasets relevant to one article, consider using a **Collection DOI** provided by repositories: -- Include this Collection DOI in the data availability statement of your manuscript so all related research objects are connected. -- For individual reactions or specific analytical data within those collections, cite them separately with their respective DOIs in both text and reference sections. +- Include this Collection DOI in the data availability statement of your manuscript so all related research objects are connected. +- For individual reactions or specific analytical data within those collections, cite them separately with their respective DOIs in both text and reference sections. ## 6. Choose a License for Your Data @@ -64,16 +64,16 @@ Licensing is crucial because it sets the rules for how others can interact with [Creative Commons licenses](https://creativecommons.org/share-your-work/cclicenses/) are widely used for sharing research data and come in various forms: -- **[CC BY](https://creativecommons.org/licenses/by/4.0/)**: Allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you. -- **[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)**: Similar to CC BY but requires new creations to carry the same license. -- **[CC BY-NC](https://creativecommons.org/licenses/by-nc/4.0/)**: Allows others to use your work non-commercially while still requiring attribution. +- **[CC BY](https://creativecommons.org/licenses/by/4.0/)**: Allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you. +- **[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)**: Similar to CC BY but requires new creations to carry the same license. +- **[CC BY-NC](https://creativecommons.org/licenses/by-nc/4.0/)**: Allows others to use your work non-commercially while still requiring attribution. ### How to Choose a License -- **Consider Your Goals**: Think about how you want others to use your data. If you encourage widespread use and adaptation, opt for more permissive licenses like [CC BY](https://creativecommons.org/licenses/by/4.0/). -- **Understand Legal Implications**: Make sure you are comfortable with what rights you are granting by choosing a particular license. +- **Consider Your Goals**: Think about how you want others to use your data. If you encourage widespread use and adaptation, opt for more permissive licenses like [CC BY](https://creativecommons.org/licenses/by/4.0/). +- **Understand Legal Implications**: Make sure you are comfortable with what rights you are granting by choosing a particular license. -- **Seek Guidance if Needed**: If you're unsure which license is best suited for your data, consult with colleagues, contact your local open science or research data management offices, or contact [NFDI4Chem's helpdesk](https://www.nfdi4chem.de/helpdesk/). Creative Commons also provides a [license chooser](https://chooser-beta.creativecommons.org/) to help you here. +- **Seek Guidance if Needed**: If you're unsure which license is best suited for your data, consult with colleagues, contact your local open science or research data management offices, or contact [NFDI4Chem's helpdesk](https://www.nfdi4chem.de/helpdesk/). Creative Commons also provides a [license chooser](https://chooser-beta.creativecommons.org/) to help you here. ### Communicate Your License Clearly diff --git a/docs/60_topics/00_topics_guide.mdx b/docs/60_topics/00_topics_guide.mdx index 8daefcc6..64ad702d 100644 --- a/docs/60_topics/00_topics_guide.mdx +++ b/docs/60_topics/00_topics_guide.mdx @@ -8,14 +8,14 @@ id: "topics_guide" The topics and concepts section offers topic-specific articles on very specific subjects in research data management and digitalization in chemistry This section is sub-divided into further categories for easy reference. ::: -For data to be interoperable and reusable, [data formats standards](62_data_formats/00_format_standards.mdx) are of utmost importance. Development here is ongoing and we will continue to add information on formats. +For data to be interoperable and reusable, [data formats standards](/docs/data_formats) are of utmost importance. Development here is ongoing and we will continue to add information on formats. In order to ensure that data are permanently referenced and cited, repositories assign unique [persistent identifiers (PIDs)](/docs/pid). By linking the journal publication to the data publication, findability of the data files for re-use is guaranteed. -Annotating data with machine-readable [metadata](63_data_description_annotation/10_metadata.mdx) plays into nearly all aspects of FAIR and ensures data are understood by machines and humans alike. Here, the core concepts of [ontologies](/docs/ontology) in one's research domain proves helpful. +Annotating data with machine-readable [metadata](/docs/metadata) plays into nearly all aspects of FAIR and ensures data are understood by machines and humans alike. Here, the core concepts of [ontologies](/docs/ontology) in one's research domain proves helpful. ## Topics in this Section -- [Identifiers](/docs/identifiers) -- [Data Formats](/docs/format_standards) -- [Data Description and Annotation](/docs/data_description_annotation) +- [Identifiers](/docs/identifiers) +- [Data Formats](/docs/data_formats) +- [Data Description and Annotation](/docs/data_description_annotation) diff --git a/docs/60_topics/61_identifiers/00_identifiers.mdx b/docs/60_topics/61_identifiers/00_identifiers.mdx index 9d086e72..d5562316 100644 --- a/docs/60_topics/61_identifiers/00_identifiers.mdx +++ b/docs/60_topics/61_identifiers/00_identifiers.mdx @@ -8,7 +8,7 @@ Identifiers are unique strings aimed to identify resources. In research data management, identifiers can include [persistent identifiers (PIDs)](/docs/pid) such as digital object identifiers [DOIs](https://www.doi.org/), [ORCID iDs](https://www.orcid.org/) and the Research Organisation Registry ([ROR](https://www.ror.org/)) to persistently refer to published articles and research datasets or software in [repositories](/docs/repositories) and researchers with their affiliation. -[Electronic Lab Notebooks (ELNs)](/docs/eln) and [archiving](/docs/data_storage) systems typically also use an identifier system. Specific to chemistry, [InChI](/docs/inchi) codes serve as a machine-readable identifier for chemical structures. A further example in chemistry is the [CAS Registry Number](https://www.cas.org/solutions/cas-custom-services/cas-registry-services), a proprietary unique identifier assigned to each chemical substance that is indexed in the proprietary [CAS database](https://www.cas.org/support/documentation/cas-databases). CAS numbers are widely used in chemical literature and databases, and they are recognized as an international standard. +[Electronic Lab Notebooks (ELNs)](/docs/eln) and [archiving](/docs/data_storage) systems typically also use an identifier system. Specific to chemistry, [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier) codes serve as a machine-readable identifier for chemical structures. A further example in chemistry is the [CAS Registry Number](https://www.cas.org/solutions/cas-custom-services/cas-registry-services), a proprietary unique identifier assigned to each chemical substance that is indexed in the proprietary [CAS database](https://www.cas.org/support/documentation/cas-databases). CAS numbers are widely used in chemical literature and databases, and they are recognized as an international standard. In terms of the [FAIR data principles](/docs/fair), identifiers should interlink scientific publications with their corresponding related dataset(s). For example, referring to the DOI of the associated published journal article in a datasets metadata via a related Identifier helps to describe and lend context to the data. Please see the [Best Practice article](/docs/best_practice) for further details. diff --git a/docs/60_topics/61_identifiers/10_pid.mdx b/docs/60_topics/61_identifiers/10_pid.mdx index ff3ef73e..dedf2d29 100644 --- a/docs/60_topics/61_identifiers/10_pid.mdx +++ b/docs/60_topics/61_identifiers/10_pid.mdx @@ -47,7 +47,7 @@ DOIs are most frequently displayed as an URL such as `https://doi.org/10.1000/18 :::danger Notice: -Most importantly, domain-independent [metadata](/docs/metadata) also including [provenance information](/docs/provenance) is attached to the DOI. This metadata follows the metadata schemes provided by registration agencies such as the [CrossRef Metadata Schema](https://www.crossref.org/documentation/schema-library/) and the [DataCite Metadata Schema](https://schema.datacite.org/). +Most importantly, domain-independent [metadata](/docs/metadata) also including provenance information is attached to the DOI. This metadata follows the metadata schemes provided by registration agencies such as the [CrossRef Metadata Schema](https://www.crossref.org/documentation/schema-library/) and the [DataCite Metadata Schema](https://schema.datacite.org/). ::: Registrants are [liable](https://support.datacite.org/docs/doi-registration-policy) to update the URL connected to the DOI if the object is moved. diff --git a/docs/60_topics/61_identifiers/20_inchi.mdx b/docs/60_topics/61_identifiers/20_inchi.mdx deleted file mode 100644 index ce4e1aea..00000000 --- a/docs/60_topics/61_identifiers/20_inchi.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: "International Chemical Identifier (InChI)" -slug: "/inchi" ---- - -import useBaseUrl from '@docusaurus/useBaseUrl'; - -# International Chemical Identifier - -![Under Construction](/img/Constr_2bl.png) - -This article is under construction. diff --git a/docs/60_topics/61_identifiers/30_cas_rn.mdx b/docs/60_topics/61_identifiers/30_cas_rn.mdx deleted file mode 100644 index 73b65052..00000000 --- a/docs/60_topics/61_identifiers/30_cas_rn.mdx +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: "CAS Registry Number" -slug: "/cas_rn" ---- - -import useBaseUrl from "@docusaurus/useBaseUrl"; - -![Under Construction](/img/Constr_2bl.png) - -This article is under construction. diff --git a/docs/60_topics/62_data_formats.mdx b/docs/60_topics/62_data_formats.mdx new file mode 100644 index 00000000..6ba9a166 --- /dev/null +++ b/docs/60_topics/62_data_formats.mdx @@ -0,0 +1,73 @@ +--- +title: "Data Formats" +slug: "/data_formats" +--- + +# Data formats + +Choosing the right data format makes your research data easier to share, understand, and reuse. For [FAIR](/docs/fair) practice, use formats that are open, well documented, and accepted by your community. + +## Why this matters + +In chemistry, data can include spectra, structures, reactions, and [metadata](/docs/metadata). The chosen format influences whether: + +- others can open your files, +- repositories accept your submission, +- your data remain usable in the long term. + +## Quick guidance for everyday work + +- Prefer open formats whenever possible. +- Keep both raw data and processed data. +- Add clear metadata, including units and context. +- Check repository requirements early. +- If instruments produce proprietary files, export an open exchange format as well. +- For structure data, store both an exchange file (e.g. [SDF](https://en.wikipedia.org/wiki/Chemical_table_file#SDF)) and identifiers ([SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System), [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier)). +- For spectroscopy, use [JCAMP-DX](https://iupac.org/what-we-do/digital-standards/jcamp-dx/) for exchange when available. + +## Recommendations by use case + +- General table-like data exchange: CSV (with clear headers and units). +- NMR data exchange: [JCAMP-DX](https://iupac.org/what-we-do/digital-standards/jcamp-dx/), [nmrML](https://nmrml.org/schema/), [NMReDATA](https://nmredatainitiative.github.io/). +- Mass spectrometry exchange and archiving: [mzML](https://en.wikipedia.org/wiki/Mass_spectrometry_data_format#mzML). +- Crystallography deposition: [CIF](https://en.wikipedia.org/wiki/Crystallographic_Information_File). +- Structure exchange: [SDF](https://en.wikipedia.org/wiki/Chemical_table_file#SDF), [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System), [InChI](https://en.wikipedia.org/wiki/International_Chemical_Identifier). +- [Chemical table files](https://en.wikipedia.org/wiki/Chemical_table_file) ([Molfile](https://en.wikipedia.org/wiki/Chemical_table_file#Molfile), rxnfile, [SDF](https://en.wikipedia.org/wiki/Chemical_table_file#SDF)): use V2000 for broad interoperability; use V3000 if you need advanced features and tool support is confirmed. +- Spectral exchange: [JCAMP-DX](https://iupac.org/what-we-do/digital-standards/jcamp-dx/) is widely supported across techniques; for very large or complex datasets, check whether a more suitable format is required. + +When possible, select formats with broad software support and active community maintenance. + +## Common pitfalls + +- Keeping only proprietary instrument files without an open export. +- Comparing [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System) strings without canonicalization. +- Omitting stereochemistry in [SMILES](https://en.wikipedia.org/wiki/Simplified_Molecular_Input_Line_Entry_System) when isomers matter. +- Sharing table files without clear units or column meaning. + +## Common chemistry formats + +| Format | Data type | Maintainer | Parent Format | Specification | +| ------------------------------------------------------------------------ | ----------------- | --------------------- | ------------------------------------------------------------ | ---------------- | +| [JCAMP-DX](https://iupac.org/what-we-do/digital-standards/jcamp-dx/) | multiple | IUPAC | ASCII, Text | open | +| AnIML | multiple | ASTM | XML | open | +| netCDF | multiple | UCAR | CDF | open | +| CSV | multiple | IETF-RFC | ASCII, Text | open | +| ASCII | multiple | (open) | | self explanatory | +| ISA | multiple | ISA Commons Community | TSV or JSON | open | +| UDM | multiple | Pistoia Alliance | XML | open | +| ADF | multiple | Allotrope | HDF5+RDF | for members | +| [mzML](https://en.wikipedia.org/wiki/Mass_spectrometry_data_format#mzML) | mass spectrometry | HUPO/PSI | XML | open | +| ANDI-MS | mass spectrometry | ASTM International | netCDF | open | +| [nmrML](https://nmrml.org/schema/) | NMR | COSMOS | XML | open | +| [NMReDATA](https://nmredatainitiative.github.io/) | NMR | NMReDATA Initiative | [SDF](https://en.wikipedia.org/wiki/Chemical_table_file#SDF) | open | +| Bruker FID | NMR | Bruker | (binary) | proprietary | +| mnova | NMR | Mestrelab | (binary) | proprietary | +| Bruker OPUS | spectroscopy | Bruker | (binary) | proprietary | +| Perkin Elmer | spectroscopy | Perkin Elmer | ASCII, Text | proprietary | +| ThermoFisher Grams | spectroscopy | ThermoFisher | binary | proprietary | + +## Sources and further information + +- [The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research](https://doi.org/10.1039/C7NP00064B) +- [Dissemination of original NMR data enhances reproducibility and integrity in chemical research](https://doi.org/10.1039/C6NP00022C) +- [NMReDATA: Tools and applications](https://doi.org/10.1002/mrc.5146) diff --git a/docs/60_topics/62_data_formats/00_format_standards.mdx b/docs/60_topics/62_data_formats/00_format_standards.mdx deleted file mode 100644 index f001c0ed..00000000 --- a/docs/60_topics/62_data_formats/00_format_standards.mdx +++ /dev/null @@ -1,140 +0,0 @@ ---- -title: "Data Format Standard" -slug: "/format_standards" -id: "format_standards" ---- - -# Data format standard {#format_standard} - -Research data is the key to any science. As part of a [FAIR](/docs/fair) practice, these data should be accompanied by valuable [metadata](/docs/metadata) and have to be exchanged in standardised and open formats. In chemistry these data include experimental parameters and measurement results, chemical structures, properties of compounds and descriptions of reactions. Whenever research data are published, stored, shared or reused, chemists have to decide on a format suitable for the purpose and consider the long-term stages of the [data life cycle](/docs/data_life_cycle). [Repositories](/docs/repositories) and databases often accept specific formats to ensure comparability and completeness of data and metadata provided. If chemists are aware of data standards during data acquisition and research documentation, later conversions will be less challenging. [Electronic lab notebooks](/docs/eln) can support the scientist in the early stages of data management. - -## Definitions {#definitions} - -A successful data format standard is defined by the data model and the data representation. A specification of a representation may contain the model implicitly. - -The **Data model** describes how data is organised, which information they contain, the data types (e.g. text, numbers, lists), the relationship between these components, and rules of component and data integrity. Conceptually, the model and its components can be organised in different ways, e.g. flat, multidimensional, as network or hierarchical. The meaning of the applied components has to be well described in an unambiguous way. [Ontologies](/docs/ontology) can be consulted to ensure a consistent usage of the components in the context of the model. As the data model is abstract, it is necessary to implement representations for it, such as a database or data file on a computer. - -Digital files containing data are only one way to represent a data model. Other **representations** would be response data from API requests or results from a database query. - -**File formats** as models can be categorised by different criteria: proprietorially vs. openly specified, binary vs. text, simple vs. complex or flat vs. n-dimensional. For long-term storage, a compact binary format may be the first choice, while for further processing with cheminformatics tools a format based on standards like Comma Separated Values (CSV), Extensible Markup Language (XML) or JavaScript Object Notation (JSON) may be more favourable due to support by most programming languages. When thinking about data strategies, open formats should be considered. Proprietary formats may prevent further reuse due to issues connected to licensing, poor documentation and support by the vendor. Complex data often need more complex formats to express the meaning and relation of data. - -The **specification** of a data file standard may describe the whole format from scratch or can utilise generic formats, which are standardised anywhere else. Such [generic formats](#generic_formats) include CSV, XML or JSON. For some of these generic formats, a formal specification format is available, called the schema format, which streamlines the specification of format derivatives. XML or JSON files can be described by schemas. If the format cannot be described formally by a general or existing schema, it is important to create an unambiguous new specification, clear in description of syntax, and grammar, as well as in definition of the components of the underlying data model. File formats should also be extendable to allow the usage of the format for new techniques and with future requirements. Data file standards not fulfilling these criteria may fork into incompatible formats or will be outdated soon and replaced by new standards. - -Any software importing or exporting a file format has to include readers and/or writers for this format, often accompanied by a validator for checking documents for adherence to the standard. Integration of any format into software is more likely if developers may rely on an **implementation**, available as a library, package or module for their programming language or environment. Thus, acceptance of a format also depends on the availability and support of implementations. Formats are more sustainable if they have a free licence and are open source. For formats described by schemas there are often well-maintained implementations for the generic format including validation. So only the schema file has to be provided. Implementations for formats, which are not openly specified and lack vendor support, are sometimes only feasible by reverse engineering, which is debatable concerning legality. Only a complete and unambiguous specification allows well-crafted implementations. - -## Generic format standards {#generic_formats} - -There are several generic formats to store data in a structured way. The format specifications do not define any domain specific elements, but some fields for document metadata, like a version, may be required to conform the specification. To create domain specific document formats, the elements including semantic descriptions have to be specified separately. -Some of these formats have a corresponding schema format, which allows a formalised description of documents. Schemas describe the data types, relations and order of elements and attributes in the element. They also have fields for description of the data to add semantic meaning in the context of the format. - -[Comma separated values (CSV)](https://datatracker.ietf.org/doc/html/rfc4180) is a simple ASCII Text format for tabular data. The values are stored as a single table with the columns separated by commas. The first line of the table may be interpreted as table header. It does not define any metadata, but these are often added as comments to the header. Although the specification does not mention comments, it is common to interpret line starting with a hash sign ("#") (or sometimes another character) as comment lines. There are variants of the format which use tabulators, semicolons or other characters to separate the values. Many vendors use the CSV format or variants of it as export format, because it is readable for humans and can be easily converted to other table formats like Excel sheets. Due to the lack of defined metadata and column descriptions it may be challenging to compare data from CSV files of different vendors as column titles or the units for values can often only be understood in the context of instrument and software. - -[Extensible Markup Language (XML)](https://www.w3.org/XML/Core/#Publications) is a markup language to structure documents. The Document is organised by elements and their attributes and the scope of an element is marked by tags for the start and the end. Elements can be nested and may include values. This allows for a representation of complex data models, including multidimensional data and hierarchies. Because an XML file is a text file it is readable for humans, but the repeated tags distract from the information contained in values. Because of the verbosity XML files tend be grow fast and are often huge compared to other formats. Thus, XML allows for large datasets to store these as [BASE64](https://doi.org/10.17487%2FRFC3548) encoded binaries. XML allows straight forward definition of specific formats by schemas and [XML Schema Definition (XSD)](https://www.w3.org/TR/xmlschema11-1/) is the most common standard for these. Implementations for reading, writing and validating XML files exist in most programming languages. For these reasons, many file formats in chemistry are based on XML. - -As XML, the [Javascript Object Notation (JSON)](https://www.ecma-international.org/wp-content/uploads/ECMA-404_2nd_edition_december_2017.pdf) was created to exchange data in the World Wide Web. Where XML has element tags for structuring the document, JSON uses braces, brackets and commas. Because of less verbosity the format is better readable for humans and files may be smaller. With [JSON Schema](http://json-schema.org/specification.html) there is a draft for a schema format to describe JSON documents. Even if JSON is less common for file formats, it is often used if data are exchanged by Application Programming Languages (API). - -Single datasets may be bundled to be stored as one unit, or large datasets may be split up to smaller files for better handling. Container formats allow to retain the relationship between the datafiles. This can be achieved by interlinking metadata in the datafiles or metafiles, by grouping files in containers or archives like [zip](https://www.iso.org/standard/60101.html) or tar, or by structured container file formats. An example for the cont approach is [HDF5](https://www.hdfgroup.org/solutions/hdf5/), which allows to combine a file system like structure with metadata. There are many implementations and tools for HDF5 and some formats for chemical data are based on it. - -## Format standards in chemistry {#chemistry_formats} - -Many formats used in chemistry are based on one of the generic formats from the last section. The following table lists some common formats used by chemists. - -| Format | Data type | Maintainer | Parent Format | Specification | -| ------------------- | ----------------- | --------------------- | ------------- | ---------------- | -| JCAMP-DX | multiple | IUPAC | ASCII, Text | open | -| AnIML | multiple | ASTM | XML | open | -| netCDF | multiple | UCAR | CDF | open | -| CSV | multiple | IETF-RFC | ASCII, Text | open | -| ASCII | multiple | (open) | | self explanatory | -| ISA | multiple | ISA Commons Community | TSV or JSON | open | -| UDM | multiple | Pistoia Alliance | XML | open | -| ADF | multiple | Allotrope | HDF5+RDF | for members | -| mzML | mass spectrometry | HUPO/PSI | XML | open | -| ANDI-MS | mass spectrometry | ASTM International | netCDF | open | -| nmrML | NMR | COSMOS | XML | open | -| NMReDATA | NMR | NMReDATA Initiative | SDF | open | -| Bruker FID | NMR | Bruker | (binary) | proprietary | -| mnova | NMR | Mestrelab | (binary) | proprietary | -| Bruker OPUS | spectroscopy | Bruker | (binary) | proprietary | -| Perkin Elmer | spectroscopy | Perkin Elmer | ASCII, Text | proprietary | -| ThermofFisher Grams | spectroscopy | ThermoFisher | binary | proprietary | - -The [JCAMP-DX format](http://www.jcamp-dx.org/protocols.html) can be applied for a wide range of spectral and analytical data. It was developed by the Joint Committee on Atomic and Molecular Physical Data (JCAMP) as a format for [IR spectroscopy](http://www.jcamp-dx.org/protocols.html#ir4.24) data since 1988 and is now maintained under the auspices of IUPAC. A general standard is proposed which can be used for different spectroscopic and spectrometric methods. Additionally, defined special standards for electron magnetic resonance spectroscopy and [nuclear magnetic resonance (NMR)](http://www.jcamp-dx.org/protocols.html#nmr), chromatography and mass spectrometry were [published](https://doi.org/10.1366%2F0003702944027840). Because the standard does not provide native support for ontology or controlled vocabulary use, and each implementation may use its own extensions, files from different sources can be incompatible. There is a Java reference implementation for this format available and there are libraries for other programming languages applicable like Python, R JavaScript and MATLAB. As JCAMP-DX is accepted as the exchange format for many analytical methods, there is a wide support by software for spectral analytics. - -The XML-based Analytical Information Markup Language (AnIML) has been created to be an ASTM International standard and covers different analytical techniques. The standard comprises schema definitions for a generic core and technique-specific documents. Thus, it is possible to define technique documents for various analytical measurements. As AnIML is fully specified by its XML schemas, it can be effortlessly implemented in any language with XML support. No reference implementation is available, but among the (few) open-source implementations Jmol/JSmol (formerly JSpecView) can import and visualise AniML documents. For developers working with the python programming language, a library is under development to create, parse and validate AniML files. There is also support by BSSN Software (now Merck) that promotes the format, often in combination with the device interface SiLA (Standardisation in Lab Automation). - -[NetCDF](https://doi.org/10.1109/38.56302) is a binary file format and software interface, mainly defined by its implementations by the Unidata community. It is an abstract model, which can be extended by self-describing objects. Thus, this model can be flexibly adopted to specific use cases. A family of ANDI (ANalytical Data Interchange) formats is specified by the ASTM, which are based on netCDF (see also ANDI-MS below). - -The [ISA (Investigation-Study-Assay) framework](https://doi.org/10.5281/zenodo.163640), originated from the bioscience community, defines the hierarchical ISA data model to store metadata on project context and study details, and analytical measurement data. As the abstract ISA data model already encourages the user to annotate any parameter or value with ontology terms, it assures well described datasets. Implementations are available as tab separated value files (ISA-Tab) or as JSON (ISA-JSON). The ISA API is a Python library implementing the model for usage with the ISA formats. The model is also applied for repositories such as MetaboLights. Moreover, journals such as ScientificData or GigaScience use the ISA data model to describe complex experimental setups covered in the manuscripts. - -### Format standards for analytical Data {#analytical_data_formats} - -Experimental data obtained with spectroscopic methods such as infrared-spectroscopy, Raman and UV/Vis-spectroscopy, are often comparatively small in size and straightforward in their structure. Vendors store the raw data in proprietary formats, either as binary data or in ASCII. These can be exported as (or converted to) Excel spreadsheets or plain text tables with x,y pairs (or similar format). A header section may include metadata. - -Users have to further process such data for their specific needs, and currently no overarching specifications exist. Repositories may convert these to a specified format, e.g. [Chemotion ELN](https://www.chemotion.net/docs) will convert text and Excel files to JCAMP-DX. - -There are a few vendor formats which are popular for data exchange: GRAMS SPC, Perkin Elmer SP and Bruker OPUS files are supported not only by the format creators, but also by other vendors and instrument-agnostic software tools for e.g. statistical analysis. - -There were efforts to create a special format for ultraviolet-visible spectroscopy data, which was called [SpectroML](https://doi.org/10.1016/S1535-5535-04-00168-6), which have now been superseded by the more general AnIML . Harmonisation between instrument vendors and adoption of an open standard still needs to be achieved. - -#### Data formats for nuclear magnetic resonance spectroscopy (NMR) {#nmr_formats} - -NMR is an indispensable analytical technique providing rich information on bonding and structure as well as molecule interaction and abundance of molecules in samples. Until now, it was common practice to publish the spectra as images in supplementary materials, regularly published as PDF files. Additionally, a list of shifts is reported, sometimes referred to as NMR text. - -However, the raw data containing the Free Induction Decays (FIDs), initially processed spectra and the instrument metadata is usually not published, which might allow for reanalysis and reuse. The importance of providing both FID raw data and extracted NMR spectra were previously demonstrated extensively. - -All instrument vendors have developed their own (binary) raw data formats. Since this FID data itself is mainly time-response data with a straight-forward structure, most of the vendor formats are supported by the software used within the NMR community. Many vendors also agreed to import and export the JCAMP-DX format, which has a [specification for FID raw data](https://doi.org/10.1366%2F0003702934067874) and is [recommended by IUPAC](https://doi.org/10.1351/pac199971081549). - -The JCAMP-DX format can also be used for exchange, import and export of multidimensional spectra. Because of the open and extensible nature of JCAMP-DX on the one hand and the lack of a controlled vocabulary on the other, there are already different flavours of the NMR format implementation, hence, validation or import might be challenging. - -Inspired by the mass spectrometry format mzML (see below) the standard [nmrML](https://doi.org/10.1021/acs.analchem.7b02795) was initially developed for metabolomics data, but can also be used for any other kind of NMR data obtained. The standard nmrML is a XML based format for FID raw data for 1D as well as 2D NMR spectra. Due to the explicit syntax specification of this format and the underlying controlled vocabulary (nmrCV), data files can be validated. It is used as a storage format for NMR data in the [Metabolights](https://doi.org/10.1002/0471250953.bi1413s53) data repository. - -The open [NMReData](https://doi.org/10.1002/mrc.4737) format is maintained by the NMReData initiative. The NMR record in NMReDATA format includes the instrument (raw) data, a SDFile and, since version 2, also spectral data in JCAMP format in a folder, which can be compressed in the zip format for data exchange. The SDFile contains the chemical structure and the actual NMReDATA as standardised SDF tags. These tags take account of chemical shifts, couplings, signal assignments and lists of 2D correlations, only to mention a few. NMReData can be used for 1D and 2D spectra and contains a core set of NMR parameters. The format allows raw data, extracted data, and structures to be recorded in one format, which is not fully supported in existing formats. It is machine- and human-readable at the same time, and allows flexibility and extensions. FAIRness is the overall principle behind it. Members of the NMReData initiative include open-source projects such as NMRShiftDB2 and Cheminfo.org, commercial NMR software vendors such as MestreLab, NOMAD, C6H6 and ACD/Labs and device vendors like Bruker [NMReData](https://doi.org/10.1002/mrc.5146). - -Recently, there were several additional open standard formats developed by the NMR community. A great effort came from protein structure determination by NMR, a field where the specifics and size of the macromolecules required special data formats. - -Derived from the self-defining STAR format are the also more protein specific [NMR-STAR](https://doi.org/10.1007/s10858-018-0220-3) which is used by the BioMagResBank (BMRB) data format and defines over 4600 data item tags describing data and metadata, which are organised in more than 300 categories and 80 category groups. The [NMR Exchange Format (NEF)](https://doi.org/10.1038/nsmb.3041) format was developed for storage of NMR data in wwPDB. It is more accessible for software developers by reducing the complexity. Additionally, it is extensible with application-specific tags. As NMR-STAR and NEF both are derived from the STAR format, they are convertible, and the only formats accepted by wwPDB and BMRB. The Collaborative Computing Project for NMR (CCPN) is developing NEF, based on the data model for usage within their protein NMR focused software tools. - -#### Data standards in Mass Spectrometry {#ms_formats} - -A distinction that is rather important in different disciplines of chemistry is whether a particular spectrum is the data of interest, or whether a set of spectra shall be represented. In the former case, text-based file formats like [JCAMP-DX](https://doi.org/10.1366%2F0003702944027840), Mascot Generic File (MGF) or National Institute for Standards and Technology Mass spectrometry (NIST MSP) may be sufficient. However, for entire runs using, e.g., LC-MS or GC-MS with hundreds of chromatography-resolved spectra, more efficient file formats have been developed. The netCDF (Network Common Data Form) based [Analytical Data Interchange Protocol for Mass Spectrometry (ANDI-MS)](https://doi.org/10.1021/ac002727b) is an ASTM International standard. It was developed initially as an Analytical Instrument Association (AIA) standard as a follow-up of the ANDI for Chromatographic Data specification. Technically, it builds upon [NetCDF](https://doi.org/10.1109/38.56302), a generic and highly efficient container format. The ANDI-MS specification defines which elements are needed to encode mass spectrometry data. - -More complex MS experiments require capturing a rich set of instrumental settings such as per-scan polarity, isolation windows and collision energies. Several formats (mzXML, mzData) had been developed in the early days of proteomics, which have been merged into mzML by the [Proteomics Standards Initiative (PSI)](https://www.psidev.info/). Despite the term Proteomics in its name, many of the PSI standards can also be used for respective analytical data from samples beyond Proteomics. The XML based mzML data format is a widely accepted standard for analytical mass spectrometry data, recommended by several societies and infrastructures for data exchange and archival. There is also a wide range of tools, including converters and spectra viewers, and software libraries to work with mzML files. The use of the PSI-MS ontology as controlled vocabulary, combined with data validators, provides excellent interoperability between consumers and producers of mzML, regardless of the instrument vendor or analysis software. - -The XML-based nature of these formats ensures that the data is readable by most, if not all, computer systems and programming languages long-term. To improve performance for fast random access and parallel processing of data, the same data model was used in several formats like [mz5](https://doi.org/10.1074/mcp.O111.011379), [Toffee](https://doi.org/10.1038/s41598-020-65015-y) and [mzMLb](https://doi.org/10.1021/acs.jproteome.0c00192) which are based on HDF5, which itself is a container format and can be considered the successor to netCDF. - -#### Data Standards in X-ray Crystallography {#xray_formats} - -Crystal structure analyses by X-ray diffraction are fundamental techniques in chemistry to determine the atomic and molecular structure of materials. These techniques measure the angles and intensities of a diffracted x-ray beam and calculate structural information from the data. In case of single-crystal measurements, the raw datasets can be very large, while other methods like powder x-ray diffraction produce only two-dimensional raw data. Therefore, data from the latter are exchanged in simple text files, exported from the instrument vendor software. - -With the [Crystallographic Information File (CIF)](https://doi.org/10.1107/S0108768102003464), there is a common exchange format for crystallographic data, which is developed and maintained by the International Union for Crystallography. The CIF is an implementation of the STAR file format and thus a text file which is organised in data blocks which are described by data names or tags. These data names are defined in plain text dictionaries, which use a controlled language and are readable for humans and computers. Besides the core dictionary with tags relevant for small-molecule and inorganic crystals, there are dictionaries for special applications like powder x-ray diffraction or macromolecular crystals (mmCIF). The possibility to extend the format by adding new dictionaries makes it ready for new methods and applications. Furthermore, with the Crystallographic Information Framework there is also a data model, which relies on the same principles as the file format and can be adapted to specific applications. - -Databases and repositories such as The [Cambridge Structural Database (CSD)](https://doi.org/10.1107/S2052520616003954) and [Crystallography Open Database (COD)](https://doi.org/10.1093/nar/gkr900) will only accept CIF as format to deposit crystallographic data. - -#### Data Standards in X-Ray Absorption and Fluorescence Spectroscopy {#abs_fluoresc_formats} - -X-Ray absorption (XAS) as well as x-ray fluorescence (XRF) generates simple spectra described by the monochromatic x-ray radiation on the abscissa and the absorption of the sample on the ordinate. The spectra can be exported to in formats based on CSV, with multiple columns, but the units (e.g. energy, wavelength), column format and the included metadata often depend on the software used for measurement on the beamline or instrument, which interferes with interoperability. To compare XAS and XRF data measured on different beamlines and devices, it is also important to include parameters of the instrument and calibration into the dataset. For larger sets of XAS data the HDF5 format is [considered as standard format](https://doi.org/10.1107/S0909049512036886), which is already used by some beamline software, such as BLISS on the ESRF. - -For the interchange of single X-Ray absorption spectra, the [XAFS Data Interchange (XDI)](https://doi.org/10.1088/1742-6596/712/1/012148) format was proposed , which combines a dictionary of relevant metadata and the data table in a text file. Thus, it is readable for humans and computers and compatible with most of the existing software accepting x,y-tables. The authors of the format also provide an implementation in C and bindings for several other programming languages such as Fortran, Perl and Python. The format was already accepted for import to the reference sample database at [Diamond Light Source](https://doi.org/10.1016/j.radphyschem.2019.108479) and the [X-ray Absorption Data Library](https://xaslib.xrayabsorption.org/elem/) of the International X-ray Absorption Society. - -There is no existing standard for XRF files, so most of the software tools provide some kind of import dialog to select matching columns and units from text files exported by the vendor software or can read data contained in HDF5 files. - -### Format standards for structural Data {#structural_data_formats} - -To describe chemical structures as a simple ASCII string there is the SMILES format, which is an open standard as OpenSMILES since 2007. For more information about SMILES and its variants read the [simplified molecular-input line-entry system (SMILES)](/docs/smiles) article in this knowledge base. - -Another one line encoding of structures, mainly intended for searching information about compounds, is the [International Chemical Identifier (InChi)](/docs/inchi), which has also an open specification and is described [separately](/docs/inchi). - -A simple Text format for chemical structures is the [XYZ format](https://openbabel.org/docs/FileFormats/XYZ_cartesian_coordinates_format.html), which only contains the coordinates for the atoms of a molecule. As the format lacks a formal specification the implementations for XYZ may differ and result in incompatible files. - -The [Chemical Table File](https://discover.3ds.com/sites/default/files/2020-08/biovia_ctfileformats_2020.pdf) was originally created by MDL Information Systems, now BIOVIA. It is now an open standard, but download of the specification requires registration. The files are text based consisting of a header and several data blocks with the structural information for the molecules. - -The structure data file (SDF) is a derivative of the chemical data file and can include multiple compounds in one file and associated dta for the elements. The SDF format was extended by NMReData to store NMR data and chemical structure in one file format. - -[The Chemical Markup Language (CML) ](http://cml.sourceforge.net/) is based on XML and is an approach to store many chemical data in an universal file format to store. Although it is capable to store also analytical data, it is mainly used for structural information and reactions. - -## Sources and further information {#sources} - -- [The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research](https://doi.org/10.1039/C7NP00064B) -- [Dissemination of original NMR data enhances reproducibility and integrity in chemical research](https://doi.org/10.1039/C6NP00022C) -- [NMReDATA: Tools and applications](https://doi.org/10.1002/mrc.5146) diff --git a/docs/60_topics/62_data_formats/10_ct_files.mdx b/docs/60_topics/62_data_formats/10_ct_files.mdx deleted file mode 100644 index 02df4c43..00000000 --- a/docs/60_topics/62_data_formats/10_ct_files.mdx +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: "Chemical Table File Formats" -slug: "/ct_files" ---- - -import useBaseUrl from "@docusaurus/useBaseUrl"; - -## Introduction - -Chemical Table files (CTfiles) are a group of text-based chemical file formats to describe collections of atoms such as molecules, intermetallic compounds, mixtures, formulations, polymers or unconnected atoms. Bonds in a bond block and atoms with their x-y-z coordinates in an atom block are listed in a connection table (Ctab). CTfiles may be also used to store additional information. The following paragraph summarises essential details on these formats. - -## Molfies, rxnfile, SDfiles and RDfiles - -The structures and relations of the most relevant CTfiles are shown in figure 1. The molfile is the most simple CTfile containing the atom coordinates as well as a Ctab of a single collection of atoms. The reaction file (rxnfile) wraps several molfiles of reactants and products, while the specification does not include agents such as solvents, reagents or catalysts, usually drawn above or below a reaction arrow. The SDfile (structure-data file) contains one or several molfiles and a data block for additional information, while the RDfile (reaction-data file) may also include rxnfiles. - -![selected chemical table file formats](/img/topics/TopicsConcepts_CT_file_formats_fig.svg) - -**Figure 1: Relationship between molfile, rxnfile, SDfile and RDfile formats following the specification. RGfiles and RDfiles containing molfiles omitted for clarity.** - -The data block may consist of several data items each starting with a data header in one line followed by the data, which might span over multiple lines. Each data item is terminated by a blank line. - -All CTfiles do not make use of controlled vocabularies or [ontologies](/docs/ontology) in the data block, but these can be easily added or linked, due to the text-based nature of CTfile formats. One example is the [controlled vocabulary](https://nmredata.org/wiki/NMReDATA_tag_format) for SDtags of the open analytical data format [NMReDATA](https://nmredata.org/). The data block might be also used to store supplementary data to enhance [machine-readability of chemical structures](/docs/machine-readable_chemical_structures). - -There are two variants of these formats in use. Due to limitations in the V2000 format, the extended V3000 format was released. This V3000 format also includes a collection block, template block, extended connection table (extended CTAB) and enhanced features on stereochemistry. The V2000 format is limited in the size of molecules by supporting up to 999 atoms, thus, it is not applicable to large molecules. Moreover, the V2000 format does not support R-groups. However, the V3000 variant is currently not supported by all tools and applications. The V2000 format should be used to maximise interoperability, especially for chemistry dealing with small molecules. - -CTfiles were created by MDL Information Systems, later acquired by Symyx Technologies, merged with Accelrys Corp and renamed to BIOVIA, which is now part of the Dassault Group. These formats can be used [without any restrictions by licences](https://www.mll-news.com/eugh-zum-urheberrechtlichen-schutz-von-software/) following European Union directive [2009/24/EC](http://data.europa.eu/eli/dir/2009/24/oj) and the EuGH judgement [C-406/10](https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:62010CJ0406:EN:HTML). - -## Sources and further information - -- [CTfile Formats, Standardizing Chemical Representation](https://discover.3ds.com/ctfile-documentation-request-form) - ---- - -Main author: [ORCID:0000-0003-4480-8661](https://orcid.org/0000-0003-4480-8661) diff --git a/docs/60_topics/62_data_formats/20_JCAMP-DX.mdx b/docs/60_topics/62_data_formats/20_JCAMP-DX.mdx deleted file mode 100644 index 8d9e8103..00000000 --- a/docs/60_topics/62_data_formats/20_JCAMP-DX.mdx +++ /dev/null @@ -1,27 +0,0 @@ ---- -slug: "/jcamp-dx" ---- - -# JCAMP-DX - -## Definition - -JCAMP-DX is an open, text-based data format family developed by the Joint Committee on Atomic and Molecular Physical Data (JCAMP) for storing and exchanging spectroscopic data; standardisation and further development are carried out under the responsibility of International Union of Pure and Applied Chemistry ([IUPAC](https://iupac.org)), which coordinates standardization and extensions of the format. It was developed to enable instrument-independent data sharing and long-term accessibility of spectral measurements. - -Although IUPAC formally oversees the standard, active development of JCAMP-DX has largely slowed since the mid-2000s, and the format now functions primarily as a de facto standard for spectroscopic data exchange." - -## Scope and Supported Techniques - -Originally introduced for infrared (IR) spectroscopy, JCAMP-DX has been extended to support a wide range of analytical techniques. These include nuclear magnetic resonance (NMR), ultraviolet/visible (UV/Vis) spectroscopy, Raman spectroscopy, mass spectrometry, and partly chromatographic data. Technique-specific recommendations define how axes, units, and experimental parameters are encoded while preserving a shared core structure across all JCAMP-DX files. - -## Format and Structure - -JCAMP-DX files are plain ASCII text files designed to be both human-readable and machine-processable. Information is organized as labeled data records (LDRs), each beginning with a double hash symbol (##). Common LDRs describe metadata such as title, origin, sample information, instrument settings, and axis units, followed by the numerical spectral data. Several data compression schemes are supported to reduce file size without compromising the integrity of the measurement. - -## Metadata and Interoperability - -A central design goal of JCAMP-DX is the inclusion of rich, self-describing metadata within the data file itself. This facilitates interoperability between instruments, software tools, repositories, and publishers. Though beeing historically older than FAIR, the format supports data reuse and long-term preservation and is well aligned with the [FAIR data principles](/docs/fair)—Findable, Accessible, Interoperable, and Reusable—widely adopted in research data management. - -## Use Cases and Limitations - -JCAMP-DX is widely used for data exchange between laboratories, instrument vendors, journals, and databases, as well as in teaching and electronic laboratory notebooks. While robust and broadly supported, the linear text-based structure can be less suitable for very large or highly complex datasets compared to newer hierarchical formats. Nevertheless, JCAMP-DX remains a stable and widely accepted standard for spectroscopic data exchange. diff --git a/docs/60_topics/62_data_formats/25_mzML.mdx b/docs/60_topics/62_data_formats/25_mzML.mdx deleted file mode 100644 index 7ad4e782..00000000 --- a/docs/60_topics/62_data_formats/25_mzML.mdx +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: "mzML" -slug: "/mz-ml" ---- - -![Under Construction](/img/Constr_2bl.png) - -This article is under construction. diff --git a/docs/60_topics/62_data_formats/30_smiles.mdx b/docs/60_topics/62_data_formats/30_smiles.mdx deleted file mode 100644 index 54dfe2c9..00000000 --- a/docs/60_topics/62_data_formats/30_smiles.mdx +++ /dev/null @@ -1,24 +0,0 @@ ---- -title: "SMILES" -slug: "/smiles" ---- - -## SMILES (Simplified Molecular Input Line Entry System) - -SMILES (Simplified Molecular Input Line Entry System) is a compact, text-based notation for representing chemical structures. It encodes molecules as linear strings using ASCII characters and is widely applied in cheminformatics for data exchange, database storage, and computational modeling. SMILES was introduced in the late 1980s and has since become a de facto standard for molecular line notation in many chemical software environments. - -## Basic syntax and examples - -In SMILES, atoms are denoted by their atomic symbols (e.g., C, O, N), and bonds are either implicit or explicitly specified using characters such as "=", "#", or ":". Single bonds are typically omitted. Branching is expressed with parentheses, and ring closures are indicated by matching digits. For example, ethanol can be written as CCO, while cyclohexane is represented as C1CCCCC1. Aromatic atoms are commonly written in lowercase letters (e.g., c1ccccc1 for benzene). SMILES also allows specification of stereochemistry through chiral flags (such as the @ symbol for tetrahedral stereocenters) and double-bond geometry markers (/, \), following defined conventions so that the relative three-dimensional arrangement of substituents can be reconstructed from the linear string. - -## Canonical and isomeric SMILES - -Two related concepts are important in practice: canonical SMILES and isomeric SMILES. Canonical SMILES provide a unique string representation for a given molecular connectivity according to a defined algorithm, facilitating database indexing and comparison. Isomeric SMILES additionally encode isotopic substitution and stereochemical information, enabling different isomers of the same connectivity to be distinguished. Explicit charge annotation, however, is a general feature of SMILES and is not restricted to isomeric forms, so charge can be specified in both canonical and non-canonical, as well as in isomeric and non-isomeric SMILES. - -## Uniqueness and limitations - -Despite its widespread use, SMILES is not intrinsically unique unless canonicalized, and the canonical form produced can depend on the implementation and algorithm used. Nevertheless, its simplicity, human readability, and compatibility with text-based workflows make SMILES a foundational format in modern computational chemistry and chemical data management, and a natural partner for other identifier systems. - -## Tool support and InChI - -Several cheminformatics toolkits, including RDKit and Open Babel, support SMILES parsing, generation, and canonicalization. Extensions such as SMARTS (for substructure searching) and SMIRKS (for reaction transformations) build upon the SMILES syntax. For persistent and standardized identification, the IUPAC International Chemical Identifier (InChI) was later developed as a complementary approach. diff --git a/docs/60_topics/62_data_formats/_category_.json b/docs/60_topics/62_data_formats/_category_.json deleted file mode 100644 index 0a8660f1..00000000 --- a/docs/60_topics/62_data_formats/_category_.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "label": "Data Formats", - "link": { - "type" : "doc", - "id" : "format_standards" - } -} diff --git a/docs/60_topics/63_data_description_annotation/00_data_description_annotation.mdx b/docs/60_topics/63_data_description_annotation/00_data_description_annotation.mdx index a2881080..bbbca725 100644 --- a/docs/60_topics/63_data_description_annotation/00_data_description_annotation.mdx +++ b/docs/60_topics/63_data_description_annotation/00_data_description_annotation.mdx @@ -8,7 +8,7 @@ To best understand a dataset at hand, whether it be for yourself, your working g 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. -Another aspect to consider is allow for data reuse is [data provenance](/docs/provenance), which can be part of the metadata. In the context of scientific data and data management, provenance means the documentation of where data material comes from and with which processes and methods it was produced. Hence, the concept of provenance is about trust, credibility and reproducibility of research and may include information such as creation date, persons creating, instruments used, software used and data processing methods applied. +Another aspect to consider for data reuse is data provenance, which can be part of the metadata. In the context of scientific data and data management, provenance means the documentation of where data material comes from and with which processes and methods it was produced. Hence, the concept of provenance is about trust, credibility and reproducibility of research and may include information such as creation date, persons creating, instruments used, software used and data processing methods applied. Furthermore, metadata used for either data description or annotation can be semantically described, employing controlled vocabularies, or better yet, [ontologies](/docs/ontology) to reduce ambiguity, making them machine-readable and machine-_understandable_. This facilitates searching and also enhances the findability of datasets for machines and humans alike. diff --git a/docs/60_topics/63_data_description_annotation/10_metadata.mdx b/docs/60_topics/63_data_description_annotation/10_metadata.mdx index 94540f8c..145ef1bf 100644 --- a/docs/60_topics/63_data_description_annotation/10_metadata.mdx +++ b/docs/60_topics/63_data_description_annotation/10_metadata.mdx @@ -6,37 +6,42 @@ slug: "/metadata" # Metadata and Minimum Information ## Metadata and their schemas + Metadata can be described as "data about data", meaning, it is data that describes data, like content of a dataset or file, or the context of this data. -More specific examples could be the title, keywords, acquisition method with a certain analytical technique, and the list continues. Metadata should be supported by controlled vocabularies (ideally [ontologies](/docs/ontology)), and/or [data formats](/docs/format_standards). +More specific examples could be the title, keywords, acquisition method with a certain analytical technique, and the list continues. Metadata should be supported by controlled vocabularies (ideally [ontologies](/docs/ontology)), and/or [data formats](/docs/data_formats). -Metadata gets more specialized as the domain it describes does, where the hierarchy of domains can correspond to a hierarchical metadata structure, enabling layers of multiple standards from more generic metadata, where it is completely domain-independent, moving to more specific ones. +Metadata gets more specialized as the domain it describes does, where the hierarchy of domains can correspond to a hierarchical metadata structure, enabling layers of multiple standards from more generic metadata, where it is completely domain-independent, moving to more specific ones. ### Domain-Independent Metadata: + Metadata can be domain-independent, focusing mostly on citation details, such as the title, the keywords, the people and institutions involved, or references to other data. Domain-independent metadata standards can be complemented by more domain-specific metadata. -* [Dublin Core](https://www.dublincore.org/specifications/dublin-core/dces/) is a more general set of fifteen elements describing networked resources. This set has been adapted and extended by other standards since its first publication in 1995. +- [Dublin Core](https://www.dublincore.org/specifications/dublin-core/dces/) is a more general set of fifteen elements describing networked resources. This set has been adapted and extended by other standards since its first publication in 1995. -* [DataCite](https://datacite.org/) is a DOI provider that provides a [schema](https://schema.datacite.org/) of core metadata for research data. The standard is community driven and tries to integrate with other standards such as Dublin Core and [ORCID Record Schema](https://info.orcid.org/documentation/integration-guide/orcid-record/). -* [OpenAIRE Guidelines for Data Archive Managers](https://guidelines.openaire.eu/en/latest/) provides an infrastructure, which facilitates interoperability between repositories adhering to those guidelines, which enhance data exposure and visibility. OpenAIRE has already adopted the DataCite [schema](https://schema.datacite.org/) but with some minor adjustments, such as accepting other persistent identifier schemes rather than the DOI, and some changes in the obligations of properties. -* [PROV](https://www.w3.org/TR/prov-overview/): The W3C standard for provenance information can be used to provide information on the origin of scientific data. -* [Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH)](http://www.openarchives.org/OAI/openarchivesprotocol.html) is a framework for harvesting metadata and can be applied to a wide variety of metadata formats. These should always include Dublin Core metadata. +- [DataCite](https://datacite.org/) is a DOI provider that provides a [schema](https://schema.datacite.org/) of core metadata for research data. The standard is community driven and tries to integrate with other standards such as Dublin Core and [ORCID Record Schema](https://info.orcid.org/documentation/integration-guide/orcid-record/). +- [OpenAIRE Guidelines for Data Archive Managers](https://guidelines.openaire.eu/en/latest/) provides an infrastructure, which facilitates interoperability between repositories adhering to those guidelines, which enhance data exposure and visibility. OpenAIRE has already adopted the DataCite [schema](https://schema.datacite.org/) but with some minor adjustments, such as accepting other persistent identifier schemes rather than the DOI, and some changes in the obligations of properties. +- [PROV](https://www.w3.org/TR/prov-overview/): The W3C standard for provenance information can be used to provide information on the origin of scientific data. +- [Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH)](http://www.openarchives.org/OAI/openarchivesprotocol.html) is a framework for harvesting metadata and can be applied to a wide variety of metadata formats. These should always include Dublin Core metadata. ### Domain-Specific Metadata: + Metadata can be domain-specific, such as acquisition method with a certain analytical technique, or the pH for a certain reaction, which don’t apply to most other domains rather than chemistry. -* [Core Scientific Metadata Model (CSMD)](http://icatproject-contrib.github.io/CSMD/) is a model for scientific studies, and it includes entity classes for facilities, users, investigations, instruments, datafiles, datasets, and samples. Within these classes most of the experimental parameters and results can be captured. Additionally, there are classes for e.g. publications, data formats, and sample types. Beside a publication of the specification as UML (Unified Modeling language) classes model definition, there is also a representation as an ontology. Future releases will focus on the integration of the [PROV](https://www.w3.org/TR/prov-overview/) model. -* [ISA (Investigation Study Assay)](https://isa-specs.readthedocs.io/en/latest/index.html) is also a metadata framework focusing on biological investigations, and it has schemas for the representation in data formats (ISA-Tab and JSON). It can be applied to many methods and allows the inclusion of ontology references for the entities. -* [IUPAC - FAIRSpec](https://github.com/IUPAC/IUPAC-FAIRSpec): It covers spectroscopic data including NMR spectroscopy. But this project is still preliminary and under development. +- [Core Scientific Metadata Model (CSMD)](http://icatproject-contrib.github.io/CSMD/) is a model for scientific studies, and it includes entity classes for facilities, users, investigations, instruments, datafiles, datasets, and samples. Within these classes most of the experimental parameters and results can be captured. Additionally, there are classes for e.g. publications, data formats, and sample types. Beside a publication of the specification as UML (Unified Modeling language) classes model definition, there is also a representation as an ontology. Future releases will focus on the integration of the [PROV](https://www.w3.org/TR/prov-overview/) model. +- [ISA (Investigation Study Assay)](https://isa-specs.readthedocs.io/en/latest/index.html) is also a metadata framework focusing on biological investigations, and it has schemas for the representation in data formats (ISA-Tab and JSON). It can be applied to many methods and allows the inclusion of ontology references for the entities. +- [IUPAC - FAIRSpec](https://github.com/IUPAC/IUPAC-FAIRSpec): It covers spectroscopic data including NMR spectroscopy. But this project is still preliminary and under development. ## Minimum information standards (MI) + Minimum information standards (MI) are guidelines regarding which metadata is required when reporting data. Furthermore, these guidelines outline which format to use for both this information as well as for the data itself. The set of MI depends on the type of data and is established to ensure that data are deposited following the FAIR principles. Therefore, minimum information is a subset of rich metadata which can accompany data. ### Minimum Information for Chemical Investigations (MIChI) -Due to the increasing amount of data produced by omics, biology and related disciplines, such as bioinformatics and biochemistry, have developed a large set of [minimum information guidelines](https://fairsharing.org/search/?q=minimum+information) for different methods. These were promoted by the [Minimum Information for Biological and Biomedical Investigations (MIBBI)](https://doi.org/10.1038/nbt.1411) project. + +Due to the increasing amount of data produced by omics, biology and related disciplines, such as bioinformatics and biochemistry, have developed a large set of [minimum information guidelines](https://fairsharing.org/search/?q=minimum+information) for different methods. These were promoted by the [Minimum Information for Biological and Biomedical Investigations (MIBBI)](https://doi.org/10.1038/nbt.1411) project. Although the explored part of the chemical space along with the chemical data produced is increasing rapidly, there are only a few attempts to define guidelines for minimum information in chemistry, e.g. [Metabolomics Standards Initiative (MSI)](https://dx.doi.org/10.1007%2Fs11306-007-0082-2) or [Collaboratory for the Multi-scale Chemical Sciences (CMCS)](https://www.researchgate.net/publication/228602526_Metadata_in_the_collaboratory_for_multi-scale_chemical_science). The NFDI4Chem will address this issue and is working on Minimum Information for Chemical Investigations (MIChI), which includes standards for methods such as mass spectrometry, nuclear magnetic resonance and other spectroscopic methods. International workshops are already being carried out in order to start the needed discussion about the MIChI. -Software projects such as [electronic lab notebooks](/docs/eln) or [repositories](/docs/repositories) often define their own layer of specific minimum metadata for chemical experiments which are based on existing standards, e.g. for metabolomics, or defined by the [data formats](/docs/format_standards) they import. +Software projects such as [electronic lab notebooks](/docs/eln) or [repositories](/docs/repositories) often define their own layer of specific minimum metadata for chemical experiments which are based on existing standards, e.g. for metabolomics, or defined by the [data formats](/docs/data_formats) they import. Existing [ontologies](/docs/ontology) are a good starting point to identify the information necessary to describe a method, results, samples, or other entities. Furthermore, controlled vocabularies and ontologies define what additional metadata is allowed in order to create rich metadata, in turn improving the data's FAIRness. Examples for formats with corresponding ontologies or a controlled vocabularies are [mzML](https://www.psidev.info/mzML), [CIF](https://www.iucr.org/resources/cif), [NeXus](https://www.nexusformat.org/), and the @@ -45,11 +50,12 @@ and the [The Chemical Analysis Metadata Platform (ChAMP)](https://champ.stuchalk.domains.unf.edu/) is a project, which focuses on defining a framework for chemical analysis methods. ## Metadata and the FAIR Principles + The [FAIR Guiding Principles](https://doi.org/10.1038/sdata.2016.18) do not only apply to data but also the associated metadata. More information can be found in the [FAIR article](/docs/fair) or on [GoFair](https://www.go-fair.org/how-to-go-fair/). Metadata, as well as the data itself, should be assigned unique [persistent identifiers (PID)](/docs/pid) to be referenced in publications and other datasets. By organizing these PIDs hierarchically, each parameter in the metadata can be referenced individually. Machine-readable metadata should be provided in a standardized format, while the metadata entities should be well-documented regarding semantics and the relations between the entities and the actual data. This can be achieved by defining the metadata as an ontology or schema, e.g. as XML or JSON. Schemas help in indexing metadata for search engines, repositories, or other data registries, and also help improve interoperability—the I in FAIR. Most of the other FAIR guidelines also apply to metadata. - ## Sources and further information + A short introductory video to Metadata can be found [here](https://www.youtube.com/embed/TnpDnflK66I) (in German): diff --git a/docs/60_topics/63_data_description_annotation/15_provenance.mdx b/docs/60_topics/63_data_description_annotation/15_provenance.mdx deleted file mode 100644 index 5757135a..00000000 --- a/docs/60_topics/63_data_description_annotation/15_provenance.mdx +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: "Data Provenance Information" -slug: "/provenance" ---- - - -![Under Construction](/img/Constr_2bl.png) - -This article is under construction. - - -## Sources and further information - -- German: [Erklärt! Daten-Provenienz](https://www.eresearch.uni-goettingen.de/de/knowledge-base/explain-data/explain-data-provenance/) diff --git a/package-lock.json b/package-lock.json index 719901c5..79c48a59 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8,10 +8,10 @@ "name": "nfdi-4-chem-knowledge-base", "version": "0.0.0", "dependencies": { - "@docusaurus/core": "^3.10.0", - "@docusaurus/faster": "^3.10.0", - "@docusaurus/plugin-sitemap": "^3.10.0", - "@docusaurus/preset-classic": "^3.10.0", + "@docusaurus/core": "^3.10.1", + "@docusaurus/faster": "^3.10.1", + "@docusaurus/plugin-sitemap": "^3.10.1", + "@docusaurus/preset-classic": "^3.10.1", "@mdx-js/react": "^3.1.1", "clsx": "^2.1.1", "docusaurus-plugin-matomo": "^0.0.8", @@ -25,9 +25,9 @@ "video-privacy": "^1.2.0" }, "devDependencies": { - "@crowdin/cli": "^4.14.1", - "@docusaurus/module-type-aliases": "^3.10.0", - "@docusaurus/types": "^3.10.0", + "@crowdin/cli": "^4.14.3", + "@docusaurus/module-type-aliases": "^3.10.1", + "@docusaurus/types": "^3.10.1", "glob": "^13.0.6", "gray-matter": "^4.0.3" }, @@ -36,15 +36,15 @@ } }, "node_modules/@algolia/abtesting": { - 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