D1_Data_Management_Plan_MSCA_2023_ExuRoots

EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 DATA MANAGEMENT PLAN PROJECTl Project number: 101153366 Project acronym: ExuRoots Project name: Biological nitrification inhibition (BNI) of the root exudates under drought stress in wheat and maize DATA MANAGEMENT PLAN Date: [11/11/2024] Version: version 1 1. Data Summary Will you re-use any existing data and what will you re-use it for? State the reasons if re-use of any existing data has been considered but discarded. What types and formats of data will the project generate or re-use? What is the purpose of the data generation or re-use and its relation to the objectives of the project? What is the expected size of the data that you intend to generate or re-use? What is the origin/provenance of the data, either generated or re-used? To whom might your data be useful ('data utility'), outside your project? In this report, we present the initial Data Management Plan (DMP) for the ExuRoots project (101153366). The purpose of this DMP is to provide a detailed description of the procedures and protocols for managing the datasets generated during the project’s lifetime. This DMP describes the main data management principles in terms of data standards and metadata, sharing, archiving, preservation, curation, and security. Every component of the DMP depends on how much and what types of data will be collected. Data volume, metadata, data quality assurance, preservation strategies, and policies should be considered. Full knowledge of these characteristics may not be available beforehand. Therefore, the plan will be updated iteratively and further refined as the project progresses. This DMP aims to enhance internal data handling and ensure that data adheres to FAIR principles (Findable, Accessible, Interoperable, and Reusable). Within this project life cycle, researchers test hypotheses and ideas by collecting data that are analyzed and visualized. These interpretations are subsequently published and shared through various channels (e.g., conference presentations, news, blogs, and tweets), frequently generating new hypotheses and ideas. Throughout the data life cycle, we devised a DMP for both the project duration and post-project phase. The DMP indicates where to locate and obtain existing data while collecting and organizing new data, ensuring data quality, describing data with metadata, utilizing data in analyses, models, and visualizations, and preserving and sharing data with others (e.g., researchers, students, decision makers), potentially generating new ideas and hypotheses (Figure 1). Ref. Ares(2024)8398581 - 26/11/2024 EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 The ExuRoots project relies significantly on metabolomics as its main discipline. Metabolomics is characterized by its capacity for large-scale data generation owing to the potential volume of data produced or the information that can be derived from thorough analysis and examination. In metabolomics research, data management is critical because of the complexity and scale of the generated data. The Metabolomics Standards Initiative (MSI) has been working to establish standardized procedures and data required for an accurate DMP in the field of metabolomics. In metabolomics, the minimum reporting standards are related to the chemical analysis aspects of the experiments, including sample preparation, experimental analysis, quality control, data preprocessing, and metabolite annotation and identification. ExuRoots mines and re-uses various published datasets in conjunction with our own generated data. Existing data should be used to conserve resources and ensure faster progress in a project. The project will utilize both open (published data and databases) and internal data sources (in-house databases), that is, the data for this project will be sourced from multiple sources, including open data repositories (Table 1). as MetaboLights, GNPS, and Metabolomics Workbench. These are supplemented by reference databases containing details on molecular structures, chemical and physical properties, biological roles, pathway networks, and, crucially, reference spectral information. Table 1. Metabolomic repositories Repository Repository links MetaboLights https://www.ebi.ac.uk/metabolights/ GNPS https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp Metabolomics Workbench https://www.metabolomicsworkbench.org/ Thus, for metabolomics, there are mainly two kinds of databases: pathway-centric and compoundcentric. Pathway-centric approaches commonly utilized in metabolomics include databases such as KEGG (Kanehisa et al., 2017), Biocyc (Caspi et al., 2016), Reactome (Naithani et al., 2020; Gupta et al., 2023), and Wikipathways (Slenter et al., 2018). Several compound-centric databases exist, including the Biological Magnetic Resonance Data Bank (BMRB), ChEBI (Hastings et al., 2013; Swainston et al., 2016), ChemSpider (Little et al., 2012), GMD (Kopka et al., 2005), MassBank (Horai et al., 2010), METLIN (Guijas et al., 2018), NIST, KNApSAcK Core DB (Afendi et al., 2012), COCONUT (Sorokina et al., 2021), LOTUS (Rutz et al., 2022), and PubChem (Kim et al., 2016). These databases will be searched for plant compounds either in root exudates or other tissues that have been involved in the induction of nitrification by microorganisms in the soil. However, to identify metabolites, reference compounds will be required by matching NMR resonance or mass spectral features to those of an unknown compound. The previously indicated databases are auto-curating repositories designed for the efficient storage and querying of mass spectral records. These databases serve as frameworks for a centralized, collaborative database of metabolite mass spectra, metadata, and associated compounds. In addition, the databases are cross-species, cross-technique, and cover metabolite structures and their reference spectra as well as their biological roles, locations and concentrations, and experimental data from metabolic experiments. However, any existing data considered for reuse will be discarded if the data do not meet the minimum quality standards, its metadata are not fully complete, or are not reviewed. Standard metadata EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 reporting offers a biological and empirical context, supports experimental replication, and allows others to reexamine and compare data. The ExuRoots project will produce different types of data in various formats. All of these formats are freely available (open source) readers and writers in various programming languages. Examples of these types of data and their descriptions are presented in the table below (Table 2). Table 2. Data types, corresponding formats, and description. Data type Format Description Experimental data Comma separated value (.csv), Excel (.xlsx), Structured plain text (.txt) Data derived from the analysis of the experimental data or literature data. BNI activity determinations Comma separated value (.csv), Excel (.xlsx), Structured plain text (.txt) Data derived from the analysis of the experimental data or literature data. Protocols .docx, .pdf, Methodologies, protocols, and workflows, standard operating procedures, multimedia and physical documents (reports, spreadsheets, presentations) Software environment data .R, .RData; .mzmineproject, .rds, .Rproj, .qmd, .html Programming scripts, software packages, etc. Data from statistical analyses will be collected in a variety of file formats, mainly R (.R) and Microsoft Excel (.xls). MS and NMR data .raw: .mzML; .cdf Mostly generated in the project, maybe some already existing data Reports, Scientific articles, News .doc, .pdf Documents for project management, dissemination, communication, public engagement, etc. Databases .msp, .mgf Mostly generated in the project, maybe some already existing data Datasets designated as open or embargoed will be publicly shared, with embargoed data becoming available after the embargo period ends. This distribution will take place via the ZENODO project profile and open data repositories, such as MetaboLights, GNPS, and Metabolomics Workbench. ExuRoots will produce various datasets of different types, including both quantitative and qualitative data. The data generated by the project through different work packages (WPs) will primarily be utilized to accomplish the objectives outlined in the project proposal. The generation of new data would be associated with the WP of the project in different ways. The possible data generated and objective (O) for each WP are detailed in Table 3. Table 3. Work packages, data and objectives WPs Generated/Re-used data Objectives/Purpose WP1. Experimental data Identifying physiological traits linked to drought stress tolerance WP2. BNI activity determinations Assessing the effect of drought stress on BNI activity WP3. MS and NMR data, Databases, Software environment data Studying root exudate composition changes during drought stress acclimation WP4. Documents, Reports Project management and progress reporting activities WP5 Presentation, news, articles, reports Dissemination, exploitation, communication, public engagement, and data management The main objective of this study was to investigate the secondary metabolites in the root exudates of diverse genotypes of wheat and maize from different geographic regions and the effect of drought on BNI activity. The datasets produced for the research objectives include all the necessary information for users to replicate the project's scientific results, such as experimental data (standardized analysis methods and protocols), observations, metabolome annotations, computational analysis, and data production and analysis codes. On the other hand, outreach, dissemination, and communication data include preprints, technical reports, conference presentations or abstracts, educational resources, and data related to outreach, dissemination, and communications. Maintain these data collection requisites are, essential for the achievement of the objectives of the project, it helps with maintaining the integrity of research, making informed decisions and ensuring quality assurance The size of the data that will be generated within the ExuRoots projects varies from a few KB for documents (protocols, data tables, R scripts), over MB for data analysis archives (standard files, data analysis R environments, figures, presentation documents, scientific articles), and up to GB for mass spectrometry (MS) files and nuclear magnetic resonance (NMR) files. The data collection is based on repeated experiments, that is, data are acquired from plants, partly more than three times for reliability EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 testing, BNI activity determinations in different nitrifying microorganisms, etc. Therefore, the sum of the generated data is in the range of several TB. The exact size that will be generated until the end of the project is not yet clearly defined, as it depends on the sampling size, number of plant root exudates, technical replicates with MS analysis, resolution of the acquired data, mode of acquisition, etc. To reduce the possible impact of the size of the generated files, they will be compressed whenever possible. The origin/provenance of this project generated data by the research group to which the MSCA researcher belonged, including other researchers, PhD students, and master’s students. Data will be generated from the experiments. The data will correspond to the physiological measurements of the plants (WP1) and BNI activities from root exudates (WP2). MS and NMR data will be obtained from the root exudates (WP3). Software tools for data processing, analysis, and visualization, such as those hosted on GitHub, will be utilized and extended (WP3). The information generated will be valuable to the broader scientific community and will become openly accessible for further analysis following publication. Through collaborations within our groups and with international scientists, such as model inter-comparison projects, the data will be extensively utilized. Additionally, this information is expected to be beneficial for various stakeholders, including those involved in climate policy advisory roles. The EU Green Deal for 2030 and related programs require agricultural production to meet certain standards, reduce the net greenhouse gas emissions (N2O included), and protect soils. This makes the data collection particularly valuable for stakeholders in the agriculture sector, such as producers who want to implement sustainable practices in their production, regional and national representatives, politicians with agro-environmental responsibilities, and seed and fertilizer companies. A portion of the acquired data is essential for addressing the current project challenges. These data remain confidential within the project as external dissemination is not warranted. Other data could be useful to academics and research institutes for reproduction and verification. Moreover, the data will be very important for the scientific community in several fields of knowledge because the project will provide quality data to answer unsolved questions on crop BNI capacity, new molecules, and the sustainability of agriculture. These include scientific publications. 2. FAIR data 2.1. Making data findable, including provisions for metadata Will data be identified by a persistent identifier? Will rich metadata be provided to allow discovery? What metadata will be created? What disciplinary or general standards will be followed? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. Will search keywords be provided in the metadata to optimize the possibility for discovery and then potential re-use? Will metadata be offered in such a way that it can be harvested and indexed? Generated and collected data will be open access and “FAIR”, that is Findable, Accessible, Interoperable and Re-usable, unless there are justified reasons for opting out, which will then be given in this DMP. The data will be identified through Digital Object Identifiers (DOIs) and repository accessions provided by the ZENODO repository and metabolomic repositories, where data will be stored. These generated datasets will be documented and published from ongoing research activities within the project. For this purpose, we will comply with the FAIR data principle to ensure that our peer-reviewed data will be useful and used by different science communities (e.g., academic and governmental) beyond the life of the ExuRoots project. Data, source code and other output underpinning measured variables and samples, in our publications will be accompanied with useful metadata, and ‘readme.txt’ style. Moreover, there are instructions on how to use the stored data in relevant open repositories (e.g., ZENODO, GitHub). Moreover, the use of “open software” means that the user can freely save, manipulate, and reuse the data. Several data points associated with the metadata were collected throughout the development of the project. Some of the data are presented in Table 4. EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 Table 4. Metadata created during the project Project step Included information Experimental Overview Experimental design, sample types, and any controls or standards used Instrument Description Instrument manufacturer, model, configuration, and settings. Sample Preparation Sample handling, preparation, and protocols, sample source, and any treatments or manipulations performed Data Acquisition Acquisition parameters, settings, and any data pre-processing steps applied during acquisition Data Analysis Integrating strategies, data transformation, statistical analysis, and any specific algorithms or software used for data processing and visualization Results Author, year, dataset standard, persistent identifier, date, file size, file type, and data related descriptions All this provided metadata will information about the quality, condition, and characteristics of the obtained data. For example, the intrinsic metadata of automatic data are acquired by machines (mass spectrometer). These high-throughput data will be accompanied by statistical analysis, machine learning algorithms, big data tools, and modelling that will have their own metadata. The general standard and framework for the description of plant metabolomic experiments and their results have been described in several studies (Jenkins et al., 2004; Data & Roadmap, 2006; Nikolau et al., 2007; Sumner et al., 2007; Haug et al., 2017). We will follow the Metabolomics Standard Initiative (MSI) developed by the Metabolomics Society, minimum information on a metabolomics experiment (MIAMET), and architecture for metabolomics (ArMet). It encompasses the entire experimental timeline from the experimental definition and description of biological source material through sample growth and preparation to the results of chemical analysis. Following these standards, the data can be verified, analyzed, interpreted, and reused by the wider scientific community. Standards are essential for mining the data across multiple experimental datasets. To enhance the discoverability of datasets and research outputs, the ExuRoots project will implement a strategy that includes keywords and readme files with research deposits. These keywords are specifically chosen to be indexable by popular search engines such as Google, thereby optimizing the accessibility of the project's resources. Associated keywords will be optimized to facilitate data exchange and enhance reuse potential unless temporary intellectual property restrictions or safety considerations are applied. The actions of project participants and the configuration of our repositories ensure that our datasets, outputs, and associated metadata are findable. The persistent identifier (DOI) enables reciprocal linking between research data and related publications. When publishing outputs in journals or uploading them to platforms, such as ZENODO or metabolomics repositories, specific metadata fields must be completed prior to file submission. These fields vary, depending on the platform used. 2.2. Making data accessible ExuRoots will adapt and implement the following open science practices to encourage reuse and further application of the results: • Data will be made accessible via open-access platforms and shared early. Project deliverables that are marked as public (PU) in the EU portal will be published on ZENODO, whereas any research output, such as datasets, publications, protocols, and abstracts, will be deposited in other online repositories (Open Research Europe). Open access will be categorized as green or gold. • Measures to make the results reproducible. Access to data and other results required for validation will be published. This includes internal peer-review processes for all deliverables. • Open peer review of the publications. If possible, our publications will be offered green and/or gold open access to Open Research Europe, enabling us not only to share the project results rapidly, but also to facilitate open, constructive research discussions to enhance their quality and relevance. Except when the owner of the data can provide a valid reason for not making it freely available. If some data are determined to be kept confidential, the reasons will be included in an updated version of the DMP. EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 Repository: The official repository for data and software codes generated by the ExuRoots project will be located on ZENODO and GitHub (https://github.com/) and stored in an open file format and with a persistent identifier (PI) under an open license. Complete datasets, excluding any information with potential exploitation value along with relevant indicators, will be accessible in metabolomic repositories. The data will possess a Digital Object Identifier (DOI) from the International DOI Foundation (IDF), which can be generated by all repositories. As previously mentioned, the ExuRoots public deliverables (marked as public, PU) in the participant portal will be published in ZENODO (also included in the GitHub personal account). If deemed to have potential exploitation value, the knowledge generated will be protected by patents, copyright, or other means, wherever appropriate. The scientific community and various stakeholders will receive the selected data and findings through conference presentations and workshop sessions. As mentioned earlier, the ExuRoots project is dedicated to following FAIR principles whenever feasible. Regarding academic publications, priority is given to submitting work to highly respected journals within the appropriate fields of study. To ensure long-term preservation and accessibility, -omics data will be deposited in public databases, which assign unique identifiers to each submission. The repositories that will be employed have certain rules and requirements. Exemptions are not granted automatically, and researchers are required to contact the repository. Data: Will all data be made openly available? If certain datasets cannot be shared (or need to be shared under restricted access conditions), explain why, clearly separating legal and contractual reasons from intentional restrictions. Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if opening their data goes against their legitimate interests or other constraints as per the Grant Agreement. If an embargo is applied to give time to publish or seek protection of the intellectual property (e.g. patents), specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible. Will the data be accessible through a free and standardized access protocol? If there are restrictions on use, how will access be provided to the data, both during and after the end of the project? How will the identity of the person accessing the data be ascertained? Is there a need for a data access committee (e.g. to evaluate/approve access requests to personal/sensitive data)? All data will be made openly available, except for the limited period to ensure the novelty of publication or longer in cases of concern related to commercial and patenting issues. Moreover, the data will be made accessible upon request. If necessary, an embargo may be applied to allow for time for publication. The duration of the embargo is determined based on the specific needs of the project, with the understanding that research data should be made available as soon as possible. The length necessary for publication varies depending on the complexity of the methodology and scientific objectives of WP. There are no restrictions, unless patents are involved. If the data are protected by the patent, the scientific publication will not be published, including the respective data, before the patent is granted (depending on the extensiveness of patent database searches and the countries intended for the patent). Therefore, the data will be made available only after the patent has been filed. Currently, in this phase of the project, there are no legal or contractual requirements to maintain the confidentiality of their information. The data will be made accessible upon request. Otherwise, the data will be accessible through a free and standardized access protocol with a computer and the Internet, that is, by clicking on a specific link. The project will provide an email (corresponding author of scientific publications) and the official telephone number of a contact person (public repositories) who can discuss access to the data, during and after the end of the project. The identity of the person accessing the data will be ascertained by requesting users to create a user account for a repository. This will allow us to authenticate the owner (or contributor) of each dataset and potentially set user-specific rights. Will the data be deposited in a trusted repository? Have you explored appropriate arrangements with the identified repository where your data will be deposited? Does the repository ensure that the data is assigned an identifier? Will the repository resolve the identifier to a digital object? EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 At this project stage, there is no data access committee that can evaluate/approve access requests to sensitive data but relies on the protocol that will be developed within the Plan for the Dissemination, Exploitation and Communication. Metadata: Will metadata be made openly available and licenced under a public domain dedication CC0, as per the Grant Agreement? If not, please clarify why. Will metadata contain information to enable the user to access the data? How long will the data remain available and findable? Will metadata be guaranteed to remain available after data is no longer available? Will documentation or reference about any software be needed to access or read the data be included? Will it be possible to include the relevant software (e.g. in open source code)? In accordance with the Grant Agreement, metadata will be freely accessible and released under a CC0 public domain dedication license. CC0 enables scientists, educators, artists, creators, and owners of copyrightor database-protected content to waive those interests in their works and thereby place them as completely as possible in the public domain, so that others may freely build upon, enhance, and reuse the works for any purpose without restriction under copyright or database law. The metadata will contain information that enables the user to access the data, which will remain available and findable in the repository as long as the repository is viable (long-term availability guarantee). In the ExuRoots project, the generated omics data will be fully documented for reproducibility and reusability. Every time it is possible, the results will be accompanied by an executed analysis script (data, metadata, scripts, and results will be stored together). These scripts contain the software version (opensource code), input data, and the employed parameters. What data and metadata vocabularies, standards, formats or methodologies will you follow to make your data interoperable to allow data exchange and re-use within and across disciplines? Will you follow community-endorsed interoperability best practices? Which ones? In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies? Will you openly publish the generated ontologies or vocabularies to allow reusing, refining or extending them? Will your data include qualified references1 to other data (e.g. other data from your project, or datasets from previous research)? To make ExuRoots data interoperable, allowing the exchange and reuse across disciplines, different metadata vocabulary, standards, formats, and methodologies will be employed. Procedures, protocols, and their explanations will be accompanied by a description and documentation written in English, using an open, accessible, and broadly applicable format and style. For this purpose, metadata vocabulary (employing special vocabulary or acronyms will be defined in the associated metadata). Rocca-Serra et al.’s (2016) recommendations and methodologies for omics data, particularly metabolomics data, will be followed. For example, the ISA-Tab format metadata standard will be used. Moreover, a mass spectrometry raw data standard, XML-based format, was employed (.mzML, .mzXML, .cdf, .mgf ). Thus, after raw data have been captured by mass spectrometers in biological LC-MS/MS experiments, the files are converted from vendor-specific binary files to open-format files for manipulation by most softwares (Holman et al., 2014). This open-source dataset file format will be uploaded to the previously mentioned and appropriate repositories in addition to journal supplements. Other experimental data and output files will be simple .txt and .csv. These formats are universally compliant with any computer system and can be read and reused across all platforms, allowing worldwide access by interested researchers and stakeholders. Finally, standard text files will be used for most numerical data and data analysis scripts, which will also consist of Jupyter notebooks or equivalents, combining textual explanations, code, and figures generated by the code. All generated data and results will include qualified 1 A qualified reference is a cross-reference that explains its intent. For example, X is regulator of Y is a much more qualified reference than X is associated with Y, or X see also Y. The goal therefore is to create as many meaningful links as possible between (meta)data resources to enrich the contextual knowledge about the data. (Source: https://www.go-fair.org/fair-principles/i3-metadata-include-qualified-references-metadata/) EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 references to other complementary project datasets and previous results. 2.3. Increase data re-use How will you provide documentation needed to validate data analysis and facilitate data re-use (e.g. readme files with information on methodology, codebooks, data cleaning, analyses, variable definitions, units of measurement, etc.)? Will your data be made freely available in the public domain to permit the widest re-use possible? Will your data be licensed using standard reuse licenses, in line with the obligations set out in the Grant Agreement? Will the data produced in the project be useable by third parties, in particular after the end of the project? Will the provenance of the data be thoroughly documented using the appropriate standards? Describe all relevant data quality assurance processes. To increase data reuse, in the ExuRoots project, documents and information about cleaning, data quality assurance procedures, methodology, as well as variable definitions, units of measurements, software dependencies, and in general data structure will be included in “readme.txt” files deposited together with data. The omics datasets generated by ExuRoots will also be accompanied by supplementary files attached to the respective publications. The ExuRoots project's methods, procedures, and protocols are available to the public, and they will be accessible through scientific articles, which will be linked to permanent repositories (e.g., ZENODO). Every published document will allow the validation of data analysis and facilitate data reuse by third parties. After all results are published, the data will be freely available to the public using a Creative Commons (CC) Attribution-Non-Commercial-Share Alike 4.0 International license. The methods or software tools needed to access and use data include the standard Microsoft package, R (freeware), and Internet Browsers (freeware). Consequently, it is expected that there are no limits or licensing requirements. To facilitate reuse by third parties, data visibility and valor will be promoted with DOIs linked to the affiliations’ publication repository, laboratories’ webpage, researcher profile pages, and researchers' ORCID IDs. Together, they should be accompanied by documentation that thoroughly indicates the provenance, origin, authors’ workflows, and methodology. For this information, the MSCA fellow will follow the appropriate standards and data quality assurance processes. The omics data at ExuRoots will be generated with respect to the applicable standard operating procedures (SOPs). Standard reference compounds will be used for quality assurance (QA) and quality control (QC) in untargeted metabolomics research. The ExuRoots project will follow the recommendations of the metabolomics quality assurance and quality control consortium (mQACC) (Lippa et al., 2022). All the measurement instruments will be operated by expert personnel or MSCA fellows. Data normalization protocols, including measurement standards, data collection templates and formats, and safe digital storage will be used. The quality of the analytical data will be guaranteed through calibration of the mass spectrometer and comparison with internal standards. It is essential to develop appropriate experimental data recording and data validation with controls, blanks, QC, randomization, blinding, and an adequate number of replicates (biological and technical). Finally, the analysis of the results and statistical analysis scripts were versioncontrolled (GitHub). 3. Other research outputs Further to the FAIR principles, DMPs should also address research outputs other than data, and should carefully consider aspects related to the allocation of resources, data security and ethical aspects. In addition to the management of data, beneficiaries should also consider and plan for the management of other research outputs that may be generated or re-used throughout their projects. Such outputs can be either digital (e.g. software, workflows, protocols, models, etc.) or physical (e.g. new materials, antibodies, reagents, samples, etc.). Beneficiaries should consider which of the questions pertaining to FAIR data above, can apply to the management of other research outputs, and should strive to provide sufficient detail on how their research outputs will be managed and shared, or made available for re-use, in line with the FAIR principles. Physical outputs, such as samples and other materials, will be stored in the respective laboratories under appropriate conditions for preservation of all material properties (indicated in the respective metadata files). Their management will be addressed specifically at the time of their delivery, in line with the Grant Agreement and FAIR principles. EU Grants: Data management plan (HE) – ExuRoots deliverable D1.1 4. Allocation of resources What will the costs be for making data or other research outputs FAIR in your project (e.g. direct and indirect costs related to storage, archiving, re-use, security, etc.) ? How will these be covered? Note that costs related to research data/output management are eligible as part of the Horizon Europe grant (if compliant with the Grant Agreement conditions) Who will be responsible for data management in your project? How will long term preservation be ensured? Discuss the necessary resources to accomplish this (costs and potential value, who decides and how, what data will be kept and for how long)? The DMP includes arrangements around responsibilities and costs for curation and reservation. The project will mostly make use of free data repositories or use organization subscriptions for domain-specific data repositories. Neither short-nor long-term storage costs were expected. The management of all data will be handled only by qualified researchers under strict confidentiality agreements, ensuring that data access, data protection, and privacy standards comply with national and European regulations. Responsibility for data management and quality assurance depends on each dataset (mainly MSCA fellow). 5. Data security What provisions are or will be in place for data security (including data recovery as well as secure storage/archiving and transfer of sensitive data)? Will the data be safely stored in trusted repositories for long term preservation and curation? Data will be collected according to standard operating procedures in open, non-proprietary, and commonly used formats by the scientific community. All the data were securely stored in different ways: • Depositing data on ZENODO allows for long-term preservation and curation in compliance with EU guidelines. In addition, they are free of charge. • Other public repositories for long-term preservation, where data will be stored, will be in place, including data recovery as well as secure storage/archiving and the transfer of sensitive data. • Electronically stored on secure servers located in locked, air-conditioned server rooms at the University of Vienna, particularly on the MOSYS server. Access is managed by group personnel. Stored data will be backed up in the drives and will only be accessible by authorized personnel. Additionally, within each analytical project, permission is granted based on the user’s role in the project. The notebook of the MSCA fellow and the host organization is in u:cloud (cloud storage), OneDrive, and MOSYS servers. The data will be stored in a minimum of two separate locations to avoid data loss. 6. Ethics Are there, or could there be, any ethics or legal issues that can have an impact on data sharing? These can also be discussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapter in the Description of the Action (DoA). Will informed consent for data sharing and long term preservation be included in questionnaires dealing with personal data? The proposed project did not involve any general ethical questions. No materials used in this project entail any risk to animals, human health, or the environment. However, the ExuRoots project is committed to absolute transparency regarding the tools and methodologies employed throughout the project. The ExuRoots project will conduct all innovative and research activities in accordance with widely accepted and established scientific standards of ethics, integrity, and reproducibility. MSCA fellow will adhere to these ethical principles in their work, including honesty, transparency, integrity, and respect for Intellectual Property Rights (IPR). The project will use the European Code of Conduct for Research Integrity as a guideline for practice.