- Thanks for using and testing our tool. Please keep bearing with us.
- The tool will be continuously developed and improved. Any feedback is welcome.
- We don't use cookie to trace users and the tool can be used offline.
+ Thanks for using and testing our tool.
Disconnect the internet or save it by "ctrl + S" will not affect the usage.
- This work has been published on MDPI Data
+ This work has been published on Data
+
+ 8th June 2024
+
+
+
Bug fix User interface bug fix
+
New feature added BBSRC DMP beta test
+
20th December 2023
@@ -3966,7 +3995,7 @@
2.1 D
- The $_PROJECT will collect and/or generate the following types of raw data : $_PHENOTYPIC, $_GENETIC, $_GENOMIC, $_METABOLOMIC, $_RNASEQ, data related $_STUDYOBJECT. In addition, the raw data will also be processed and modified using analytical pipelines, which may yield different results or include ad hoc data analysis parts. #if$_DATAPLANT These pipelines will be
+ The $_PROJECT will collect and/or generate the following types of raw data : $_PHENOTYPIC, $_GENETIC, $_IMAGE, $_RNASEQ, $_GENOMIC, $_METABOLOMIC, $_PROTEoMIC, $_TARGETED, $_MODELS, $_CODE, $_EXCEL, $_CLONED-DNA data related $_STUDYOBJECT. In addition, the raw data will also be processed and modified using analytical pipelines, which may yield different results or include ad hoc data analysis parts. #if$_DATAPLANT These pipelines will be
tracked in the DataPLANT ARC#endif$_DATAPLANT. Therefore, care will be taken to document and archive these resources (including the analytical pipelines) as well#if$_DATAPLANT relying on the expertise
in the DataPLANT consortium#endif$_DATAPLANT.
@@ -3975,8 +4004,7 @@
2.1 D
Will you re-use any existing data and how?
- The project builds on existing data sets and relies on them. #if$_RNASEQ For example, without a proper genomic reference it is very difficult to analyze next-generation sequencing (NGS) data sets.#endif$_RNASEQ It is also important to
- include existing data-sets on the expression and metabolic behavior of the $_STUDYOBJECT, and on existing background knowledge. #if$_PARTNERS of the partners. #endif$_PARTNERS
+ The project builds on existing data sets and relies on them. #if$_RNASEQ For example, without a proper genomic reference it is very difficult to analyze next-generation sequencing (NGS) data sets.#endif$_RNASEQ It is also important to include existing data-sets on the expression and metabolic behavior of the $_STUDYOBJECT, and on existing background knowledge. #if$_PARTNERS of the partners. #endif$_PARTNERS
Genomic references can be gathered from reference databases for genomes/ and sequences, like the US National Center for Biotechnology Information: NCBI, European Bioinformatics Institute: EBI; DNA Data
Bank of Japan: DDBJ. Furthermore, prior 'unstructured' data in the form of publications and data contained therein will be used for decision making.
@@ -4488,10 +4516,7 @@
1. Data Summary
- The $_PROJECT will collect and/or generate the following types of raw data: $_PHENOTYPIC, $_GENETIC, $_GENOMIC, $_METABOLOMIC, $_RNASEQ, data about $_STUDYOBJECT. In addition, derived data from the
- original raw data sets will also be collected. This is important, as different analytical pipelines might yield different results or include ad-hoc data analysis parts,#if$_DATAPLANT and these pipelines will be
- tracked in the DataPLANT ARC#endif$_DATAPLANT. Therefore, specific care will be taken to document and archive these resources (including the analytic pipelines) as well#if$_DATAPLANT relying on the vast expertise
- in the DataPLANT consortium#endif$_DATAPLANT.
+ The $_PROJECT will collect and/or generate the following types of raw data : $_PHENOTYPIC, $_GENETIC, $_IMAGE, $_RNASEQ, $_GENOMIC, $_METABOLOMIC, $_PROTEoMIC, $_TARGETED, $_MODELS, $_CODE, $_EXCEL, $_CLONED-DNA data related $_STUDYOBJECT. In addition, the raw data will also be processed and modified using analytical pipelines, which may yield different results or include ad hoc data analysis parts. #if$_DATAPLANT These pipelines will be tracked in the DataPLANT ARC#endif$_DATAPLANT. Therefore, care will be taken to document and archive these resources (including the analytical pipelines) as well#if$_DATAPLANT relying on the expertise in the DataPLANT consortium#endif$_DATAPLANT.
@@ -5220,7 +5245,7 @@
- #endif$_DATAPLANT #if$_OTHERSTANDARDS $_OTHERSTANDARDINPUT #endif$_OTHERSTANDARDS
+ #if$_OTHERSTANDARDS Other standards will also be used, such as $_OTHERSTANDARDINPUT. #endif$_OTHERSTANDARDS
@@ -8084,7 +8109,7 @@
-
+
Datenmanagementplan
Projektname: $_PROJECT
Forschungsförderer: Bundesministerium für Bildung und Forschung
@@ -8128,7 +8153,22 @@
-
+
+
+ Data management plan of $_PROJECT for BBSRC
+
+
Data Areas and Data Types – The $_PROJECT will collect and/or generate the following types of raw data : $_PHENOTYPIC, $_GENETIC, $_IMAGE, $_RNASEQ, $_GENOMIC, $_METABOLOMIC, $_PROTEoMIC, $_TARGETED, $_MODELS, $_CODE, $_EXCEL, $_CLONED-DNA data related $_STUDYOBJECT. In addition, the raw data will also be processed and modified using analytical pipelines, which may yield different results or include ad hoc data analysis parts. #if$_DATAPLANT These pipelines will be tracked in the DataPLANT ARC#endif$_DATAPLANT. Therefore, care will be taken to document and archive these resources (including the analytical pipelines) as well#if$_DATAPLANT relying on the expertise in the DataPLANT consortium#endif$_DATAPLANT.
+
+ We expect to generate raw data in the range of $_RAWDATA GB of data. The size of the derived data will be about $_DERIVEDDATA GB.
+
+
Standards and Metadata – We will use Investigation, Study, Assay (ISA) specification for metadata creation. #if$_RNASEQ|$_GENOMIC For specific data (e.g., RNASeq or genomic data), we use metadata templates from the end-point repositories. #if$_MINSEQE The Minimum Information About a Next-generation Sequencing Experiment (MinSEQe) will also be used. #endif$_MINSEQE #endif$_RNASEQ|$_GENOMIC #if$_METABOLOMIC Metabolights submission compliant standards will be used for metabolomic data where this is acccepted by the consortium partners.#issuewarning some Metabolomics partners considers Metabolights not an accepted standard#endissuewarning#endif$_METABOLOMIC As a part of plant research community, we use #if$_MIAPPE MIAPPE for phenotyping data in the broadest sense, but we will also be rely on #endif$_MIAPPE specific SOPs for additional annotations #if$_DATAPLANT that consider advanced DataPLANT annotation and ontologies. #endif$_DATAPLANT
+
Reuse of published data – The project builds on existing data sets and relies on them. #if$_RNASEQ For example, without a proper genomic reference it is very difficult to analyze next-generation sequencing (NGS) data sets.#endif$_RNASEQ It is also important to include existing data-sets on the expression and metabolic behavior of the $_STUDYOBJECT, and on existing background knowledge. #if$_PARTNERS of the partners. #endif$_PARTNERS Genomic references can be gathered from reference databases for genomes/ and sequences, like the US National Center for Biotechnology Information: NCBI, European Bioinformatics Institute: EBI; DNA Data Bank of Japan: DDBJ. Furthermore, prior 'unstructured' data in the form of publications and data contained therein will be used for decision making.
+
Secondary Use – The data will initially benefit the $_PROJECT partners, but will also be made available to selected stakeholders closely involved in the project, and then the scientific community working on $_STUDYOBJECT. $_DATAUTILITY In addition, the general public interested in $_STUDYOBJECT can also use the data after publication. The data will be disseminated according to the $_PROJECT's dissemination and communication plan, #if$_DATAPLANT which aligns with DataPLANT platform or other means#endif$_DATAPLANT
+
+
Methods for Data Sharing – Specialized repositories will be used where appropriate, such as INSDC (GenBank, EBI, DDBJ) for nucleotide sequence data, PIR/UniProt/SWISS-PROT for proteins, PDB for protein structures, GEO for transcriptomics, PRIDE for proteomics data, and METLIN for metabolomics data. For unstructured and less standardized data (e.g., experimental phenotypic measurements), these will be annotated with metadata and if complete allocated a digital object identifier (DOI). #if$_DATAPLANT Whole datasets will also be wrapped into an ARC with allocated DOIs. The ARC and the converters provided by DataPLANT will ensure that the upload into the endpoint repositories is fast and easy. #endif$_DATAPLANT
Proprietary Data – Open public data will be used whenever possible.
Timeframes #if$_early The data will be published as soon as possible to guarantee reusability. #endif$_early #if$_ipissue IP issues will be checked before publication. #endif$_ipissue All consortium partners will be encouraged to make data available before publication, openly and/or under pre-publication agreements #if$_GENOMIC such as those started in Fort Lauderdale and set forth by the Toronto International Data Release Workshop. #endif$_GENOMIC This will be implemented as soon as IP-related checks are complete.
Format of the Final Dataset – Whenever possible, data will be stored in common and openly defined formats including all the necessary metadata. By default, no proprietary formats will be used. However Microsoft Excel files (according to ISO/IEC 29500-1:2016) might be used as intermediates by the consortium#if$_DATAPLANT and by some ARC components#endif$_DATAPLANT. In addition, text files might be edited in text processor files, but will be shared as pdf.
+