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1 Basic Information:

1.1 What is the project name or acronym?

Who is most likely to benefit from the data? 🛈

1.3 Other 🛈 DMP Metadata

1.4 Please select from the following options

2. What kind of data will you handle?

2.1 Where will you submit your data as endpoints?

3. How much data are you likely to generate?

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4. Are any of the following standards relevant to your project?

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4.1 Will you adhere to any high level metadata submission standards?

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4.2 Project data will be published:

4.4 Which national, funder, sectorial, or departmental data management procedures do you follow, and which national research data infrastructure do you use?

5. Do you intend to use data visualization in your project?

























The project aim should be a apart of a sentence.

Example 1: aims at creating a computational model of carbon and water flow within a whole plant architecture


Example 2: aims at generating data management plan with minimal effort and making the data as open as possible

The project object = target.

Example 1: carbon and water flow in plants


Example 2: data management plan

Here is the space for additional sentence.

Example 1: Industry, politicians and students can also use the data for different purposes.


Example 2: The data acquired in the project can be used by a wide range of people with different purpose.

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Data officers are also known as data stewards and curator.

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MIAPPE is an open, community driven, data standard designed to harmonize data from plant phenotyping experiments. MIAPPE provides a specification including a checklist and a data model of metadata required to adequately describe plant phenotyping experiments. More information can be found on: MIAPPE Homepage

The Genomic Standards Consortium (GSC) has developed a standard called the Minimum Information about any (x) Sequence (MIxS) for reporting marker gene sequences. This standard includes the Minimum Information about a Marker Gene Sequence (MIMARKS) and introduces 'environmental packages' to describe the environment from which biological samples originate. MIxS aims to unify standards for describing sequence data, providing a single point of entry for the scientific community to access GSC checklists. Adoption of MIxS will improve the analysis of genetic diversity documented by extensive DNA sequencing efforts across various ecosystems. More information can be found on: MIxS Homepage

Use for eukaryotic genomic sequences. Organism must have lineage Eukaryota. More information can be found on: MIGS publication

Minimum Information about a Genome Sequence: Organelle. More information can be found on: MIGS publication

MIGS/MIMS (Minimum Information About a (Meta)Genome Sequence) outlines a conceptual structure for extending the core information that has been traditionally captured by the INSDC (DDBJ/EMBL/Genbank) to describe genomic and metagenomic sequences. The MIMS extension describes key aspects of environmental context. More information can be found on: MIGS/MIMS publication

Use for any type of marker gene sequences, eg, 16S, 18S, 23S, 28S rRNA or COI obtained from cultured or voucher-identifiable specimens. More information can be found on: MIMARKSSpecimen publication

Use for any type of marker gene sequences, eg, 16S, 18S, 23S, 28S rRNA or COI obtained directly from the environment, without culturing or identification of the organisms. More information can be found on: MIMARKSSurvey publication

The Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity. More information can be found on: MISAG publication

The Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity. More information can be found on: MIMAG publication

the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. More information can be found on: MIAME publication

MIAMET (Minimum Information About a METabolomics experiment) is an analogous standard, but adapted to the specifics of metabolomics. More information can be found on: MIAMET publication

MINSEQE describes the Minimum Information about a high-throughput nucleotide SEQuencing Experiment that is needed to enable the unambiguous interpretation and facilitate reproduction of the results of the experiment. By analogy to the MIAME guidelines for microarray experiments, adherence to the MINSEQE guidelines will improve integration of multiple experiments across different modalities, thereby maximising the value of high-throughput research. More information can be found on: MINSEQE publication

Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. REMBI is the result of the bioimaging community coming together to develop metadata standards that describe the imaging data itself, together with supporting metadata such as those describing the biological study and sample. It will also help enable automated data harvesting using machine learning techniques. More information can be found on: REMBI publication

The MIAPE guidelines should require sufficient information about a dataset and its experimental context to allow a reader to understand and critically evaluate the interpretation and conclusions, and to support their experimental corroboration. Practicability. More information can be found on: MIAPE publication

Adherence to MIMIx, the minimum information required for reporting a molecular interaction experiment, will result in publications of increased clarity and usefulness to the scientific community and will support the rapid, systematic capture of molecular interaction data in public databases, thereby improving access to valuable interaction data. More information can be found on: MIMIx publication

The Dublin Core vocabulary, also known as the Dublin Core Metadata Terms (DCMT), is a general purpose metadata vocabulary for describing resources of any type. It was first developed for describing web content in the early days of the World Wide Web. The Dublin Core Metadata Initiative (DCMI) is responsible for maintaining the Dublin Core vocabulary. More information can be found on: Dublin Core official homepage

Darwin Core is a standard maintained by the Darwin Core Maintenance Interest Group. It includes a glossary of terms (in other contexts these might be called properties, elements, fields, columns, attributes, or concepts) intended to facilitate the sharing of information about biological diversity by providing identifiers, labels, and definitions. Darwin Core is primarily based on taxa, their occurrence in nature as documented by observations, specimens, samples, and related information. More information can be found on: Darwin Core official homepage

Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond. More information can be found on: Schema.org official homepage

Bioschemas aims to improve the Findability on the Web of life sciences resources such as datasets, software, and training materials. It does this by encouraging people in the life sciences to use Schema.org markup in their websites so that they are indexable by search engines and other services. Bioschemas encourages the consistent use of markup to ease the consumption of the contained markup across many sites. This structured information then makes it easier to discover, collate, and analyse distributed resources. More information can be found on: Bioschemas official homepage

The MARC formats are standards for the representation and exchange of data in machine-readable form.The Network Development and MARC Standards Office (NDMSO), supported by the MARC Advisory Committee, is responsible for maintaining and developing the format. MARC 21 is also available in an XML structure as well as in two extended format variants in the context of MARC local fields. More information can be found on: MARC official homepage

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