Research Data Management

Research Data Management Mariarita de Luca, Laboratory of Data Engineering, Area Science Park 07-08-09/10/2025 10.5281/zenodo.17302188 OUTLINE ✓Introduction ✓Data Professional Profiles ✓Data and Research Data ✓Research Data Management (RDM) ✓Q&A R e s e a r c h e r a t L a b o r a t o r y o f D a t a E n g i n e e r i n g ( L A D E ) •Nano Foundries and Fine Analysis –Digital Infrastructure (NFFA-DI) project. •FAIR management and infrastructure for research data. R e s e a r c h M a n a g e r a n d A d m i n i s t r a t o r •Open Science practices, Research Data Management (DMP, FAIR Data,...). •Management of H2020 and Horizon Europe research projects. S e n i o r P o s t - d o c t o r a l F e l l o w s h i p : •Development of mathematical models for porous and swelling materials and active materials (nematic liquid crystals). •Collaboration in the MathLab laboratory P o s t - d o c t o r a l F e l l o w s h i p : •Development of mathematical models for soft mechanical materials. •Teaching assistant for the course of Mathematical Analysis, Calculus and continuum mechanics. •Collaboration in the MOX Laboratory P h D i n A p p l i e d M a t h e m a t i c s •Development of mathematical models for mechanical properties of arterial wall. •Visiting student at Department of Mechanics and Materials at University of Pittsburgh Work experience A black and white logo Description automatically generated with low confidence A black and white logo Description automatically generated with low confidence Research Institute for Technological Innovation (RIT) Three laboratories active in creating an integrated system of research infrastructures and platforms. They provide, in an open access mode, knowledge and services aimed at carrying out experimental testing and applied and industrial research projects. GENOMICS AND EPIGENOMICS LABORATORY LAGE ELECTRON MICROSCOPY LABORATORY LAME DATA ENGINEERING LABORATORY LADE DATA INFRASTRUCTURE DATA MANGEMENT AND CURATION DATA SCIENCE AREA SCIENCE PARK (TRIESTE) Scientific interests M a t h e m a t i c a l m o d e l s f o r s o f t - a c t i v e m a t e r i a l s •Elasticity within large deformation framework (non-linear models) •Deformation of active-smart materials (swelling materials, nematic elastomers, …) M. de Luca, A. DeSimone. Elastomeric Gels: A Model and First Results. Innovative Numerical Approaches for Multi-Field and Multi-Scale Problems. Lecture Notes in Applied and Computational Mechanics, vol 81. Springer, Cham. (2016) https://doi.org/10.1007/978-3-319-39022-2_4 M. de Luca, A. Petelin, M. Copic and A. DeSimone, "Sub-stripe pattern formation in liquid crystal elastomers: Experimental observations and numerical simulations", JMPS, 61 (2013) 2161 – 2177 https://doi.org/10.1016/j.jmps.2013.07.002 •Do I have access to my publications? •Where are my data? •Can I reproduce my numerical simulations? What about my data and my publications? Image by Elisa from Pixabay •Do you have access to your publications? •Where are your data? •Can your experiment/research be reproduced? What about YOUR data and YOUR publications? Image by Elisa from Pixabay Data Professions Data Professions: Who Does What? Role Main Focus Typical Environment Key Skills Data Steward Governance, quality, FAIRness, metadata Academia (University Department), EOSC RDM, FAIR, policy, metadata standards Data Curator Validation , enrichment, preservation Academia (Repositories), industry Metadata, ontologies, preservation , standards Data Manager Operational management of datasets and pipelines, Accessibility Academia, industry DMP, workflows, compliance Data Engineer Data pipelines, automation, interoperability Academia, industry ETL, APIs, data infrastructures Data Scientist Evaluates fitness-for-use of datasets ( fairness), analytics, modelling , ML/AI Academia, industry Statistics , Python, AI models Which role fits your current or future work best? Research Data Research data are all the raw materials collected, processed and analyzed in the undertaking of research. They are the evidential basis that substantiates published research findings. They may be: •primary data are generated or collected by the researcher fromffirst-hand sources; •secondary data are collected from existing sources and processed and analyzed as part of the research activity. Whether you can reuse secondary/existing data, and the conditions under which they can be reused, depends on copyright, licenses and Terms of Use. RESEARCH DATA Data & standards —Research Data Management (kuleuven.be) Raw - processed - analyzed data ❑Raw data are the original data that you have collected but not yet processed or analyzed. E.g.: audio files, archives, observations, field notes, data from experiments, etc. ❑Processed data are the data that you have digitized, translated, transcribed, cleaned, validated, checked and/or anonymized. ❑Analyzed data are based on the raw and processed data, e.g.: models, graphs, tables, texts, etc. They are intended to present your conclusions. Data & standards —Research Data Management (kuleuven.be) Research data also include all the information about the means necessary to generate data or replicate results, such as computer code, experimental methods and instruments used, and essential interpretive and contextual information, e.g. specifications of variables. Analogue or non-digital data Physical materials are also research data. Some examples: Art (paintings, sculptures, photographs, films, … ) Maps and drawings (building plans,…) Organisms (laboratory animals, plants, insects, bacteria, … ) Paper-based questionnaires Stones and minerals Electronic components (lab on a chip, … ) Nucleic acid, protein samples Data & standards —Research Data Management (kuleuven.be) "I do not have any research data" In some disciplines (e.g. theoretical research such as mathematics or philosophy), you may wonder whether you have actually research data. Research data is all information, generated as part of the scientific process, on which scientific conclusions are based. Physical items such as manuscripts, books, maps and artefacts are also research data as well as (handwritten) notes, proofs, annotations etc. that support the conclusions in your published work. Data & standards —Research Data Management (kuleuven.be) All is data (What Are Data? - Karin Olson, 2021) Observational Facts recorded directly in real time from the physical and social environment, e.g. measurements collected by weather sensors, species abundance surveys, archaeological samples, brain scan images, experience and opinion surveys in the social sciences. These data are often unique to time and place and by definition cannot be reproduced. Experimental Data collected as the outputs of field or laboratory experiments and complex analytical processes, e.g. clinical trial data, chemical analyses of physical samples, DNA sequencing of organic material, field trial results. These data are generally in principle reproducible, assuming the experimental conditions can be replicated. Data classification (based on collection method) Data & standards —Research Data Management Simulation Data generated by means of computational 'virtual experiments', often used to model complex systems and processes, e.g. climate and weather simulations, models of market processes. These data are usually reproducible, given information about the model, the code and computing environment used to execute the model, and any input conditions. This information may in fact be more important that the output data. Data classification (based on collection method) Data & standards —Research Data Management Derived or compiled Datasets produced by processing or combining source data, e.g. databases compiled by extraction of information from multiple secondary sources, collections of digitized materials, corpora collected by means of text mining. Reference Published and curated data, usually existing as part of managed collections, e.g. national statistics archives, crystallographic databases, gene banks. Data classification (based on collection method) Data & standards —Research Data Management Bibliographic references Bibliographic references and publications that are used as background or to support an argument are not considered as research data in an RDM context. But, if bibliographic sources are important sources of information for your research and you do content analysis, add annotation or coding or if you make extensive use of quotes, then that is considered research data. A bibliographic database with such coding, annotation and textual extracts can then be listed as a dataset in your DMP. Data & standards —Research Data Management Data or not Data? Picture from «The Turing Way» 10.5281/zenodo.3332807 REMEMBER ✓If you reuse secondary/existing data, they should be cited in your publications. ✓Patient data can only be reused if the informed consent and ethics approval allows reuse for your purpose. ✓Data that are publicly available on the internet still belong to someone and may have use restrictions. ✓Existing agreements with third parties may put severe limitations on reuse and sharing. Data & standards —Research Data Management (kuleuven.be) In research, Big Data represent large-scale, heterogeneous datasets —often multimodal or cross-disciplinary —whose management and analysis require coordinated infrastructures, metadata standards, and scalable tools. Big Data are often described through five main dimensions —each representing both a challenge and an opportunity. • Volume – Massive amounts of data → scalability and storage challenges. • Velocity – The speed at which data are generated and processed → real-time systems and streaming analytics. • Variety – Multiple data types (structured, semi-structured, unstructured) → interoperability and harmonization. • Veracity – Data quality and reliability → need for validation and provenance tracking. • Value – Extracting insights and meaning → turning data into knowledge and impact. “The 5 V’s remind us that managing data is not only about size — it’s about complexity, speed, and meaning.” The 5 V’s of Big Data 5V‟s of Big Data Attributes Image generated with AI Data Pipeline Adata pipeline is a system designed to automate the flow of data from various sources to a destination, enabling efficient collection, processing, and delivery of data for analysis and decisionmaking. Data pipelines are essential for handling the increasing volume, variety, and velocity of data in modern organizations. They streamline the movement of data across systems, ensuring accuracy, consistency, and scalability. Key Functions of a Data Pipeline 1.Data Collection: A pipeline gathers data from multiple sources, such as databases, APIs, or streaming platforms, ensuring seamless integration of structured, semi-structured, and unstructured data. 2.Data Cleaning/Processing: It cleans, validates, and transforms raw data into a standardized format. This step ensures data quality and prepares it for analysis or storage. 3.Data Analysis: Analysis of data through algorithms or tools to extract knowledge from data. 4.Data Delivery (Visualization/Report): The pipeline delivers the processed data to tools or systems, such as business intelligence platforms or machine learning models, for actionable insights. information in binary form and its associated metadata Digital object Image by yuelanliu from Pixabay Globally, unique and long-lasting references to digital objects (such as data, publications and other research outputs) or non-digital objects such as researchers, research institutions, grants, etc. Typically, such an identifier is not only persistent but actionable: you can plug it into a web browser and be taken to the identified source. Persistent Identifiers (PIDs) programme-guide_horizon_en.pdf (europa.eu) The Open Researcher and Contributor ID (ORCID) is a non-proprietary alphanumeric identifier for the unique identification of scientists and other authors of scientific literature. Definition: Wikipedia https://orcid.org/ Picture from «The Turing Way» 10.5281/zenodo.3332807 A DOI is a digital identifier of an object, any object (physical, digital, or abstract). Designed to be used by humans as well as machines, DOIs identify objects persistently. They allow things to be uniquely identified and accessed reliably. You know what you have, where it is, and others can track it too. Picture from «The Turing Way» 10.5281/zenodo.3332807 KEY POINTS FOR RDM ➢Easier to analyze organized and documented data ➢Find data more easily ➢Don’t drown in irrelevant data ➢Don’t lose data ➢Get credit for your data ➢Avoid accusations of misconduct RMD (harvard.edu) Picture from «The Turing Way» 10.5281/zenodo.3332807 D E S I G N & P L A N N I N G S H A R E & R E U S E P R O C E S S I N G & A N A L Y S I S C O L L E C T & C R E A T E Bongaerts, N., & Della Chiesa, S. (2022). Research Data Management Lifecycle (2.0). Zenodo. https://doi.org/10.5281/zenodo.6602006 Research Data Lifecycle D E S I G N & P L A N N I N G S H A R E & R E U S E P R O C E S S I N G & A N A L Y S I S C O L L E C T & C R E A T E Project proposal Project approved DMP draft Resource planning High level policies Coaching guidance Data retrieval Data type Scripts algorithms Storage backup Metadata standards Data organization Raw data & Active Analysis Ready data Workflows Quality control Data preserve Provenance Version control Data sharing Code sharing IP & IPR Licenses Metadata sharing Research Data Lifecycle Bongaerts, N., & Della Chiesa, S. (2022). Research Data Management Lifecycle (2.0). Zenodo. https://doi.org/10.5281/zenodo.6602006 ➢Design & Planning : Data Steward, Data Manager Planning ahead to data needs, that proposers are likely to encounter during the project, is a best practice. Before defining how to manage your data, identify: • Which funding instrument supports your research (e.g., Horizon Europe, ERC, national grants, institutional funds). • Which data-related policies and legal obligations apply accordingly. Funding Scheme, Policies and Legal Framework Typical Data Requirements by Funding Scheme: • Horizon Europe – Mandatory Data Management Plan (DMP), open access to publications and data (“as open as possible, as closed as necessary”). • National / PNRR / PRIN – Open access following national Open Science plans (PNSA, MUR guidelines). • Institutional / Private Grants – Institutional or funder-specific data policies, retention and IPR rules. • Industrial / Confidential Projects – Restricted access, NDAs, IP ownership and security constraints. Funding Scheme, Policies and Legal Framework • Identify the funding body’s data policy and legal framework early in the project. • Align your DMP with funder and institutional requirements. • Verify any data compliance that applies to your project. • Clarify ownership, confidentiality, and licensing before data collection. “A well-designed DMP starts by understanding who funds your research and which policies govern your data.” Key Actions during Planning Design and Planning When planning a research project, it is important to consider how integrity, reproducibility and FAIRness can be incorporated into the research design Corso: Essentials 4 Data Support (English) - Public | DANS (moodlecloud.com) RESEARCH DESIGN: •What is the research question? •Which research method is suitable? •How do you prepare your research for reproducibility and reuse? How do you prepare your research for reproducibility and reuse? - 1 -Define the reproducibility objectives of the research project If you are part of a bigger project, you can get information from the project data policy, the funder data policy and your Institution data policy. - Imagine what a 'reproducibility declaration' in an article could look like A'data availability statement’ is not enough, it does not tell another researcher how he or she could reproduce the research. With a reproducibility statement a research can. If you think beforehand about how such a reproducibility declaration could look, that will tell a lot about how to design and document the research in order to make reproducibility possible. Collect and Create - 2 Corso: Essentials 4 Data Support (English) - Public | DANS (moodlecloud.com) Collecting your own research data: ✓By simulation (test models) Climate models and economic models, for example. The results of simulations can usually be reproduced. It is more useful to store the model and metadata itself than the data resulting from the simulations. ✓By data processing Combining, reprocessing, (re)grouping etc. of data created before. The processing can be reproduced if it is correctly documented. ✓By researching sources For example, data deriving from archive and literature research in order to compose texts, or series of 'measurable' data from archived material, manuscripts and (professional) publications. Specialist queries of large linguistic databases are also an example. Foto di Peggy und Marco Lachmann-Anke da Pixabay STRUCTURED and UNSTRUCTURED DATA Images generated with AI DATA TYPE AND FORMAT Format Description Categories - Sustainability of Digital Formats | Library of Congress (loc.gov) File formats | DANS FILE ORGANIZATION Foto di OpenClipart-Vectors da Pixabay File Naming Conventions If you want your data to be findable, easy to interpret and effective to work in a collaborative way, then it is important to store the data in a structured, consistent manner and to provide it with the necessary data documentation and metadata. Pay attention to access authorization to your data. FILE NAMING CONVENTIONS - 1 File Naming Conventions A file naming convention is a framework for naming your files in a way that describes what they contain and how they relate to other files. File naming conventions help you stay organized and quickly identify your files. In a shared or collaborative group file-sharing setting, it will help others more easily navigate your files. It is essential to establish a convention before you begin collecting files or data in order to prevent a backlog of unorganized content that will lead to misplaced or lost data! PHD Comics: A story in file names Image: xkcd. "Documents." Shared under CC-BY-NC License. File Naming Conventions | Data Management (harvard.edu) - TIPS for naming convention No naming convention: •Test data 2016.xlsx •Meeting notes Jan 17.doc •Notes Eric.txt •Final FINAL last version.docx With a naming convention: •20160104_ProjectA_Ex1Test1_SmithE_v1.xlsx •20160104_ProjectA_MeetingNotes_SmithE_v2.docx •ExperimentName_InstrumentName_CaptureTime_ImageID.tif The version date (use ISO 8601 format: YYYYMMDD or YYYY-MM-DD) FILE NAMING CONVENTIONS - 1 Metadata: Data about data, infomation to describe how data are created/collected, analyzed, by whom, with which instruments, with what methodology, …. For each specific discipline, we need proper metadata to correctly describe the data. Metadata is any description of a resource that can serve the purpose of enabling findability and/or reusability and/or interpretation and/or assessment of that resource. Metadata - 1 Metadata must be machine readable, then they must be written according to standards, ontologies, taxonomies and semantic metadata. Metadata - 2 Semantic metadata is data that describes the meaning of that data or content. In other words, it is making explicit the meaning of the metadata so that machines, and not just humans, can infer or interpret information about that metadata. An ontology is a description of data structure–of classes, properties, and relationships in a domain of knowledge. It is meant to serve as a basis for instances of knowledge graphs, ensuring data consistency and understanding of the data model. Data taxonomy is the classification of data into hierarchical groups to create structure, standardize terminology, and popularize a dataset within an organization. Semantic Data Integration | SpringerLink An ontology is a formal representation of knowledge in a domain that takes advantage of first-order logic, standardized relationships. Ontologies consist of a set of classes that represent concepts defining a field and the relationships among these classes. These are distinguished from other ways of organizing knowledge, such as controlled vocabularies and taxonomies, by the richness and expressiveness of relationships. ONTOLOGY https://doi.org/10.3389/neuro.01.007.2009 A controlled vocabulary can be thought of as the backbone of an ontology. It is a set of terms in a subject domain that may have been given definitions and unique identifiers, but which has no explicit relationships among these terms. A taxonomy adds to a controlled vocabulary by further organizing terms according to one or more classification criteria. An ontology builds upon a taxonomy by adding the ability to define other relationships between entities beyond identifier, definition, and place in the taxonomic hierarchy. Relationships such as “part of” allow entities*within the ontology to be related to one another across the taxonomic hierarchy. These relationships themselves are entities that can be rigorously defined based on what is required to describe the knowledge domain. https://doi.org/10.3389/neuro.01.007.2009 ONTOLOGY vs TAXONOMY • Integrity : Protect data from loss, corruption, or accidental overwriting. • Security :Apply access control, authentication, and encryption where necessary. • Versioning :Track file changes using naming conventions or version control (e.g., Git). • Organization :Use structured directories and capture metadata from the start. • Backup :Follow the 3–2–1 rule: 3 copies, on 2 media types, with 1 off-site or cloud backup. KEY PRINCIPLES: FOR STORAGE STRATEGIES • Institutional Servers :Managed by IT, secure, and regularly backed up. • NAS (Network-Attached Storage) : Shared local environments, suitable for collaboration. • Cloud Storage (institutional/research cloud) :For distributed teams, ensure GDPR compliance. • Encrypted External Drives : For fieldwork or offline collection, ensure safe handling. • Electronic Lab Notebooks / Data Capture Tools :Enable real-time data entry and metadata integration. STORAGE OPTIONS D E S I G N & P L A N N I N G S H A R E & R E U S E P R O C E S S I N G & A N A L Y S I S C O L L E C T & C R E A T E Project proposal Project approved DMP draft Resource planning High level policies Coaching guidance Data retrieval Data type Scripts algorithms Storage backup Metadata standards Data organization Raw data & Active Analysis Ready data Workflows Quality control Data preserve Provenance Version control Data sharing Code sharing IP & IPR Licenses Metadata sharing Research Data Lifecycle Bongaerts, N., & Della Chiesa, S. (2022). Research Data Management Lifecycle (2.0). Zenodo. https://doi.org/10.5281/zenodo.6602006 ➢Design & Planning : Data Steward, Data Manager ➢Collect & Create : Data Curator, Data Engineer ➢Processing & Analysis: Data Curator, Data Scientist Once data are collected or created, they must be processed, analyzed, and documented to ensure reliability, reproducibility, and value. This stage transforms raw data into meaningful results while preserving traceability and integrity. PROCESSING & ANALYSIS: FROM RAW DATA TO KNOWLEDGE • Data Processing & Cleaning :Correct, normalize, and structure data for analysis (Python, R, validation, workflow automation). • Data Analysis & Modelling :Extract insights, test hypotheses, build models (statistics, ML, reproducible environments). • Documentation & Reproducibility :Record methods, parameters, versions, and provenance (Jupyter, …). “Processing and analysis are not just computational tasks, they are acts of Responsible Research Data Management ensuring that results can be verified and reused.” PROCESSING & ANALYSIS: KEY COMPONENTS The Turing Way: A handbook for reproducible, ethical and collaborative research Software versioning and Git Master Data Management and Curation · GitHub Maintaining code and data versioning is essential for reproducibility. Tools like Git (with platforms such as GitHub, GitLab, or institutional repositories) allow tracking every change, collaborating transparently, and linking code to specific data versions used in analysis. Picture from «The Turing Way» 10.5281/zenodo.3332807 Recommendations for providing high-quality data Data.europa.eu data quality guidelines - Publications Office of the EU Complete your data Mark null values explicitly as such!!! Use STANDARS for time https://www.iso.org/iso-8601-date-and-time-format.html AREA SCIENCE PARK Copyright protection •When you create an original literary, scientific and artistic work, such as poems, articles, films, songs or sculptures, you automatically have copyright protection, which starts from the moment you create your work, so you don't need to go through any formal application process. •Nobody apart from you has the right to make the work public or reproduce it. •However, you may need to advise other people that you are the author of that work. You can attach a copyright notice to your work –such as the "all rights reserved" text, or the © symbol –together with the year the work was created. •You can also register your copyright via a dedicated service provider, which can be useful to prove the existence of your work at a certain point in time. •In EU countries, copyright protects your intellectual property until 70 years after your death or 70 years after the death of the last surviving author in the case of a work of joint authorship. Copyright in the EU: How to get copyright protection - Your Europe (europa.eu) Creative Commons licenses https://commons.wikimedia.org/wiki/File:Creative_Commons_Licenses.png. Used under a Creative Commons Attribution-Share Alike 3.0 Unported license There are 6 types of Creative Commons licenses and divided according to the right that the author intends to keep for himself. Choose a License (creativecommons.org) AREA SCIENCE PARK Copyright •Copyright protects © the work from all improper uses and reproductions that have not been authorized by the author. •With copyright "all rights reserved“. •Creative Commons (CC) licenses allow the author to retain only some of the rights to the work while allowing free reuse, but also free dissemination of the work without any need for authorization. •Creative Commons licenses say that "some rights reserved". Creative Commons licenses Diritto d’autore e copyright: quali sono le differenze (lexplain.it) Protection of Research Data Research data are likely to be a ‘bundle’ of different types of information and content, sourced from third parties, or created by the researcher. Research data may be factual and/or creative. Data as such, like facts, principles, mathematical concepts and methods are not protected by copyright. However, there are cases in which data, not as such but part of collections, can be protected. Additionally, whereas data as such are not protected by copyright, that does not mean data are not protected by other laws (e.g. confidential information or personal data). How do I know if my research data is protected? (openaire.eu) Does the law protect Research Data? •Copyright protection covers creative works. •Confidentiality protects confidential information. This might be imposed by a contract or if the information is marked confidential. •Data Subject Rights arise in information that identifies individuals and are recognized by data protection laws in the EU (GDPR). ➢Primary (or raw) Data is not intellectual work, no copyright applies! ➢Protection is on databases and not on data. Source by E. Lazzeri, F. Di Donato, FAIR principles and Open data, 10.5281/zenodo.4450515 Foto di OpenClipart-Vectors da Pixabay How do I know if my research data is protected? (openaire.eu) Research data are not YOUR data, They are obtained with public funds. Based on copyright definition, they are not protected by copyrights and then they belong to public domain (CC0 license or equivalent). Fact Sheet on Creative Commons & Open Science (zenodo.org) Database law protection Database is defined as a collection of independent works, data or other materials arranged in a systematic or methodical way. Databases are automatically protected by law if: 1. the selection or arrangement of the contents are the author’s own intellectual creation – in which case copyright protection applies to the structure of the database (not to its content); or 2. they qualify for a special IP (Intellectual Property) right (called the sui generis database right (SGDR)) because there has been a substantial (economic) investment in obtaining, verifying or presenting the contents of a database (e.g. a database of poetry titles). Simone Aliprandi DOI 10.5281/zenodo.6575821, Aspetti legali degli open data: la guida definitiva The content of a database can also be composed of copyright protected works in the first place, such as a database of scholarly articles. A database may be protected by up to 3 different rights that regulate 3 different uses. Toolkit for Researchers on Legal Issues (https://doi.org/10.5281/zenodo.2574619) What license should be applied to research data? If your research data, funded by public money, qualifies as a creative work (literary work such as a journal article or a software), then CC BY 4.0 is usually the best choice. The use of the CC Share Alike (SA) is also compatible with the Open Access definition and reinforced in Plan S licensing guidance for publications. Non-commercial CC should be avoided as it is not Open Access compliant. Non-derivative CC is a tricky issue and should be avoided, especially if you do not know what you are doing. That said, it may not be incompatible with the Open Access definition. Toolkit for Researchers on Legal Issues (https://doi.org/10.5281/zenodo.2574619) DO YOU NEED HELP? ASK FOR SUPPORT OpenAIRE Get support on general open science practices [email protected] www.openaire.eu Research Infrastructures Get support from your specific domain Research Infrastructure www.esfri.eu Be supported at national level from a team of experts in Open Science and EOSC related fields Coming soon: https://www.icdi.it/it/atti vita/tf-cc In case your institution has a structured open science strategy and dedicated support! Your Institution Competence Center Slide from Emma Lazzeri, 10.5281/zenodo.4326562. Digital Repository Adigital repository is a collection of online resources that organizes, search, and provides a specific, persistent location for digital items. There are two primary types of digital repositories: •institutional (specific to an institution) •disciplinary (focused on a particular subject area). Adigital repository is a collection of online resources that organizes, search, and provides a specific, persistent location for digital items. There are two primary types of digital repositories: •institutional (specific to an institution) •disciplinary (focused on a particular subject area). How to find a repository? Repositories approuved by European Commission