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src/docs/DataManagementPlan.md

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+---
+layout: docs
+title: Data Management Plan
+published: 2022-05-09
+Author: ?
+add toc: true
+add sidebar: true
+Article Status: Publishable
+---
+
+# Data Management Plan
+
+- [Advantages of a DMP](#Advantages-of-a-DMP)
+- [Elements of a DMP](#Elements-of-a-DMP)
+- [DataPLANT's Data Management Plan Generator](#DataPLANT's-Data-Management-Plan-Generator)
+
+
+A data management plan (DMP) structures the handling of research data in a scientific project and describes how you are planning to deal with the data during and after the end of the project. Many third-party funders, such as DFG, Horizon Europe, or BMBF expect you to provide information on the handling of research data as part of your funding application. While a formal DMP is only required in rare cases, a DMP is almost exclusively beneficial for your work on a research project. 
+
+## Advantages of a DMP
+A DMP allows you to track the current status and special features of your project throughout its entire lifecycle. Thus, it is helpful for the administration and to keep an overview. 
+Scientific practice shows that you generally profit more from the effort spent on better structuring, documentation, and organization of your research data, if you [FAIRify] *cross-link* and thereby, lower the hurdles of reusing your datasets.
+Additional personnel, technical, or infrastructural resources required for compliance with your DMP can usually be claimed in third-party funding applications. 
+
+- Enables access to certain funding lines	
+- Facilitates documentation for reporting requirements	
+- Simplifies your own subsequent use of data by specifying clear structures                                                            	 
+- Data publication can be considered an independent publication	 
+- Reduces the risk of data loss	 
+- Increases the chance that data carriers and file formats are still readable after 10 years
+- Improves knowledge management in the event of personnel changes
+- Increases the number of citations through regulated reuse/licensing for third parties
+
+
+## Elements of a DMP
+When setting up your own data management plan, it can be advantageous to be aware of the current best practices, including a broad range of data management aspects. Additionally, many funders have their own requirements and/or templates for a DMP, which you should be aware of. A DMP can contain the following information:
+
+- Overview: A collection of objectives, sponsors, partners, project managers, and duration of your project.
+- Data description: A description of the information to be gathered, the type and scale of the data that will be generated, and information on how data will be generated or collected.
+- Existing data: A survey of existing data relevant to the project and a discussion of whether and how these data will be integrated.
+- Format: Formats in which the data will be generated, maintained, and made available, including a justification for the procedural and archival appropriateness of those formats.
+- Metadata: A description of the metadata to be provided along with the generated data, and a discussion of the metadata standards used.
+- Storage and backup: Storage methods and backup procedures for the data, including the physical and virtual resources and facilities that will be used for the effective preservation and storage of the research data, incl. file naming and versioning as well as synchronization and collaborative working.
+- Security:	A description of technical and procedural protections for information, including confidential information, and how permissions, restrictions, and embargoes will be enforced.
+- Resources: An overview of details of your anticipated costs for DMP compliance, including staff, metadata creation, digital curation services, and archiving.
+- Responsibility: Names of the individuals responsible for data management in your research project.
+- Intellectual property rights:	Entities or persons who will hold the intellectual property rights to the data, and how IP will be protected if necessary. Any copyright constraints (e.g., copyrighted data collection instruments) should be noted.
+- Access and sharing: A description of how data will be shared, including access procedures, embargo periods, technical mechanisms for dissemination and whether access will be open or granted only to specific user groups. You should also provide a timeframe for data sharing and publishing.
+- Archiving and preservation: The procedures in place or envisioned for long-term archiving and preservation of the data, including succession plans for your data in case the expected archiving entity is closed.
+
+
+## DataPLANT's Data Management Plan Generator
+You can write your DMPs offline by using the downloaded template of a funding agency in a text document format. However, a number of web-based DMP tools are currently available that greatly facilitate the process, as they use different templates. 
+DataPLANT helps the community by providing a Data Management Plan Generator on their own with various building blocks, available under https://nfdi4plants.org/dmpg/. The tool provides guidance throughout the writing process, as it is a living document. When opening the tool, users first need to select the type of document they want to generate: a detailed european DMP, a short proposal DMP, or a detailed guide through the project. Depending on the choice, the tool will restructure the document and point out errors when information is missing. The tool also gives the possibility to save the document in different formats during or after completion
+
+![DMPG](DMPG.png)
+
+
+## Sources and further information
+- [DataPLANT's DMPG](https://nfdi4plants.org/dmpg/)
+- [DFG: Handling of Research Data](https://www.dfg.de/en/research_funding/principles_dfg_funding/research_data/index.html)
+- [forschungsdaten.info - Der Datenmanagementplan](https://www.forschungsdaten.info/themen/informieren-und-planen/datenmanagementplan/) 
+- [RDMkit - Data management plan](https://rdmkit.elixir-europe.org/data_management_plan.html)
+- [Elements of a DMP](https://www.icpsr.umich.edu/web/pages/datamanagement/dmp/elements.html)

src/docs/DataPublication.md

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+---
+layout: docs
+title: Data Publications
+published: 2022-05-09
+Author: ?
+add toc: true
+add sidebar: true
+Article Status: Publishable
+---
+
+# Data Publications
+- [Benefits of data publications](#Benefits-of-data-publications)
+- [Data papers & data journals](#Data-papers-&-data-journals)
+- [Data journals](#Data-journals)
+- [Data repositories](#Data-repositories)
+- [How does DataPLANT support me in data publication?](#How-does-DataPLANT-support-me-in-data-publications?)
+
+
+
+Data Publication, or data publishing, is defined as releasing your research data in a published form. This will allow others to access and use your datasets. Writing a manuscript can sometimes consume a lot of time. Some researchers might find this process tedious if they only want to publish certain data, which they considered as interesting or impactful during and after collection. Data publishing is an integral part of the open science movement. In general, the main goal is to evolve data to first class research outputs, driven by a number of initiatives. This enables datasets to be cited similarly to other research publication types, such as articles or books, enabling producers of datasets to gain academic credit for their work.
+
+There are a several criteria that should be fulfilled during publication of your dataset:
+1. Of course, your data needs to be hosted in a repository to make it available for everyone. Various repositories exist, which have  been developed to support data publication, e.g. [Zenodo](https://zenodo.org/), including general, but also domain-specific data repositories exist.
+2. Your dataset needs to be well annotated, allowing other researchs to understand and reuse your data. 
+3. Your dataset needs to be assigned a persistent identifier (PID), such as a DOI. This can be assigned directly on the repository or with the help of a publication service, such as [Invenio](https://inveniosoftware.org/products/rdm/). The identifier will others to cite your dataset.
+4. In case the publisher validates your data, which is not always the case, he will review your metadata annotation to ensure comprehensibility.
+
+There is also the possibility for publishing a data paper about the dataset, which may be published as a preprint, in a journal, or in a data journal that is dedicated to supporting data papers. The data may be hosted by the journal or hosted separately in a data repository. For more information see [data papers & data journals](#Data-papers-&-data-journals)
+
+![Data publishing](tileshop.jpg)
+
+**Figure 1:** During publication, datasets are typically deposited in a repository to make them available, documented to support reproduction and reuse, and assigned an identifier to facilitate citation. Some, but not all, publishers review datasets to validate them.
+
+
+## Benefits of data publications
+The motivations for publishing data may range for a desire to make research more accessible, to enable citability of datasets, or research funder or publisher mandates that require open data publishing. Some scientists might argue that they would uncomfortable about publishing their dataset, as it could allow people to copy my work from the web and plagiarize it. However, most print-based science journals are available online nowadays, so the potential of copying is already present. Additionally, solutions to preserve privacy within data publishing has been proposed, including privacy protection algorithms, data ”masking” methods, and regional privacy level calculation algorithm. 
+In general, the advantages of data publications prevail. Here is a list of some potential benefits you might get from publishing your dataset:
+- Data can be reused for similar and new purposes
+- Data can be integrated with other data to create new data resources
+- Invitations to collaborate
+- Invitations to provide consultancy
+- Greater citation rate
+- Citation of data publications is likely to increase citations of related research papers
+- Wider recognition among peers
+- Overall acceleration of science and better science
+- ... 
+
+## Data papers & data journals
+Data papers or data articles are “scholarly publications of a searchable metadata document describing a particular on-line accessible dataset, or a group of datasets, published in accordance to the standard academic practices”. The intent of a data paper is to offer a descriptive information on the related dataset(s) focusing on data collection and distinguishing features, rather than on data processing and analysis. Thereby, their aim is answering questions like "What data was published?", "How was the data collected?", or "Who collected the data?" As data papers are considered academic publications, just as other types of papers, they allow scientists sharing data to receive credit and thus, upgrading the value of data sharing. This provides not only an additional incentive to share data, but also increases metadata quality and reusability of the shared data.
+
+Data papers are supported by a variety of journals, of which some are "true" data journals, i.e. they are dedicated to publish data papers only, while the majority are mixed journals meaning they publish a number of articles types, including data papers. A list of data journals can be found [here](https://www.researchdata.uni-jena.de/en/information/data-publication) 
+
+
+## How does DataPLANT support me in data publications?
+- ARC
+- DataHub
+- Invenio
+
+## Sources and further information
+- [Data publication: towards a database of everything](https://doi.org/10.1186/1756-0500-2-113)
+- [Motivating Online Publication of Data](https://doi.org/10.1525/bio.2009.59.5.9)
+- [Data publication consensus and controversies](https://dx.doi.org/10.12688%2Ff1000research.3979.3)
+- [Making Data a First Class Scientific Output](https://doi.org/10.2218/ijdc.v7i1.218)
+- [The data paper](https://dx.doi.org/10.1186%2F1471-2105-12-S15-S2)