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2 | https://oberred.eu/ | |||||||||||||||||||||||||
3 | This reference framework is a generic framework that should be adapted to local situations (countries, disciplines, projects, ...) | |||||||||||||||||||||||||
4 | GENERAL COMPETENCE | COMPETENCE | SKILLS | METADATA | EXERCISES | ESCO | ||||||||||||||||||||
5 | Skills grouping (according to a cycle of the data) | Full name of the badge | Badge N° | Abbreviated (if needed) badge name for the badge graphic | General skills targeted | Possible Badge's metadata (to be adapted/detailed according to discipline, area of work and/or project) | Possible validation exercises To validate those skills you might use… | Additional comments to the skills targeted + local and national adaptations | Link to ESCO framework (if available) | Link to other frameworks | ||||||||||||||||
6 | Plan and design | Data models | A1 | Data models | I can identify the different data types, formats, models and standards | I make the difference between "working data" ("hot data") and "archived data" ("cold data ») I know the difference between "embedded data", "underlying date", "raw data" and "gathering data" | - Fill out a quiz with different kind of data - Define which data are "hot" or "cold" - Collect several examples of embedded, underlying, raw data used in your daily research life | |||||||||||||||||||
7 | Data policies and open data | A2 | Data policies | I am familiar with national and international research data policies | I know the legal principles that govern the opening of research data (in my country, internationally) I know the principles of the GDPR (Europe) and what it implies for the collection of data I am familiar with intellectual property and consortium agreements | - From a list of legal principles tick those that apply to research data - Fill out a quiz consisting of a set of different data types where you will answer whether the data is or is not GDPR compliant | the badge issuer will have to modify or adapt the expected competences according to national legislation | |||||||||||||||||||
8 | F.A.I.R. data | A3 | FAIR data | I know the FAIR principles for outputs | I understand how FAIR principles apply to my field of study, discipline, area of work | - List practical tasks you have completed when working with research data and match them to the correct 15 FAIR principles. - Verify if project descriptions from a given list meet the FAIR principles. | ||||||||||||||||||||
9 | Data Management Plan (DMP) | A4 | DMP | I know what a DMP is I know how to write a DMP according to the existing best practices | I know the DMP template for a Horizon Europe program or other funding agency. I know my institutional DMP template. I know the best practices for DMP existing in my field of study, discipline, area of work. I know where to find a DMP model | - Find a DMP template and adapt it to the specifics of their project. - Match different elements of the DMP to their definitions. - Showcase previous work experience in writing DMPs. | ||||||||||||||||||||
10 | Choose a repository | A5 | Choose repository | I know where to find a repository relevant for my outputs I know the main criteria for selecting a repository or data warehouse I know repositories' certificates and standards | I know Trustworthy Data Repositories Requirements I'm able to identify repositories (at national, European, and international level) to upload research data according to my discipline | - Assign examples of metadata to their categories. - Find two repositories that would be suitable for your project. - Upload your dataset to the selected repository. | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | |||||||||||||||||||
11 | Metadata and PID standards | A6 | Metadata & PIDs | I know service that deliver persistent Identifiers (PIDs) for my outputs I can properly attribute metadata and PIDs to my dataset | I am able to distinguish between different PIDs (e.g. DOI, Handle, ORCID, SWHID) and know services which assign them I know discipline-related metadata to tag/index my data (e.g. controlled vocabularies) | - Make a list of services delivering PIDs - Complete a quiz identifying different elements of metadata relevant to the data created or collected within your discipline | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | |||||||||||||||||||
12 | Data collection and management | Collect data | B7 | Collect data | I know how to collect data originating from sources relevant to my area of work (e.g. digital sources, observation, field work) I can identify relevant sources for data collection | I can create data relevant for my area of work. I can retrieve existing data relevant for my area of work I can enhance data | - Complete a quiz where you answer questions about whether the format is open or closed. - Complete a task in which you indicate which digital tools from the list are used to convert data. - Complete a task in which you convert the data you have collected into one open digital format of your choice. | |||||||||||||||||||
13 | Tools for data collection | B8 | Tools for data | I know and can use data collection systems | I know and can use data collection systems (e.g. SQL applications, SPSS, Omeka) relevant for my field of study, discipline area of work I am able to change tools in time if needed I can use advanced functions of a spreadsheet I can manage a digitized corpus of data (full text, images, video, etc.) with the appropriate tools | - Identify and tick tools for data collection from a list of programmes and systems. - Give examples of using formulas or advanced functions of spreadsheets and/or other data collection tools and systems in your work experience. | ||||||||||||||||||||
14 | Data Description | Describe data | C9 | Describe data | I know and understand best practices for data documentation I can describe my dataset in a narrative way | I can write data documentation that facilitates interoperability I know best practices for data documentation in my field of study, discipline, area of work | - Prepare a narrative description of your dataset. - Complete a task in which you match disciplines to their corresponding thesauri. - From the list of good practices, select those that relate to data documentation. | |||||||||||||||||||
15 | Data formatting and storage | Organise data | D10 | Organise data | I know how to structure and organise existing datasets I know how to adjust data sizes, formats for different needs | I can organise data in different categories (e.g. working data and cold data) to facilitate proper research data workflows I am able to use the formats relevant to my discipline I know how to adjust the size of files I manage | - Complete a quiz where you choose relevant data formats for specific examples of data and projects. - Showcase previous experience in categorising and organising data you have worked with. | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | ||||||||||||||||||
16 | Store data | D11 | Store data | I am familiar with different data storage software solutions for active, operational data I can manage software solutions for data storage (databases, servers, virtual machines) | I can adjust the data storage solution to my dataset. I can manage a database system. I can configure cloud-based solutions for data storage | - Write a text that presents the advantages and disadvantages of the database chosen for the project or explains why no database is used. - Create and upload a diagram that represents the data integration process. | To be adapted by the issuer according to the need and the local context. For example "I know how to install and configure a MySQL database". | |||||||||||||||||||
17 | Secure data | D12 | Secure data | I apply information security policies. I know how to develop information security strategy | I know the security requirements of my organization I am able to keep software for data storage up to date | - Complete a quiz with basic security requirements of your organisation. - Give example(s) of the ways in which you have adopted information security strategy in previous projects. | ||||||||||||||||||||
18 | Data quality assurance | Clean and normalise data | E13 | Clean & normalise | I can clean and normalise a dataset according to the requirements of my laboratory, discipline or other framework | I can detect duplicates, incomplete, erroneous data I can use programming solutions for data reconciliation (fuzzy matching etc.). I know and can use software for data cleaning and normalisation (e.g. OpenRefine). | - Give an example of a routine associated with the project. - From the set of standards choose the ones that are compatible with the policy of your institution. | http://data.europa.eu/esco/skill/50b100ea-74fd-4706-99db-3e4ca55e51b8 http://data.europa.eu/esco/skill/07889c08-7220-47c8-96f7-6068fbea00dc | ||||||||||||||||||
19 | Assess data quality | E14 | Assess data | I understand the concept of data quality I can determine the quality criteria for the data (well formatted, well indexed, up to date, etc.). I can implement or enforce the quality process | I can enforce open standards to ensure the quality of my data according to the standards of my discipline I can set up procedures for data quality assessments and use tools for that | - Complete a quiz where you verify the quality of data in a showcased dataset. - Identify examples of data quality assessment tools for your discipline. | ||||||||||||||||||||
20 | Data processing and analysis | Integrate data | F15 | Integrate data | I can aggregate data. I can integrate and harmonize various types of data | I am able to convert between different data formats, standards. I am skilled in integrating data from various sources. I am able to enrich datasets to provide interoperability | - Identify examples of aggregated data. - Enrich your data and explain step-by-step how you did it. | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | ||||||||||||||||||
21 | Analyse data | F16 | Analyse data | I can investigate datasets in order to identify relevant patterns, trends I am able to retrieve information for data analysis I am able to draw conclusions from data analysis | I am familiar with pattern recognition and/or statistical analysis. I know and can use tools supporting data analysis I know how to interpret the results of data analysis | - Showcase previous work experience in data analysis. - Make a list of tools relevant to different types of data analysis within your areas of interest. | ||||||||||||||||||||
22 | Data Reproducibility | F17 | Reproducibility | I am able to ensure the reproducibility of research based on a given dataset | I can use tools, methods, standards and prepare their documentation which guarantees reproducibility in obtaining research results | - Choose from the list the appropriate tool for specific needs. - Explain how to make your results reproducible for co-workers. | ||||||||||||||||||||
23 | Visualise data | F18 | Visualise | I understand the role of data visualization and can visualise data with digital tools | I am able to use a relevant data visualisation software to visualise and search my data I can propose an interface to visualise my data | - Prepare a list of examples of software for data visualization. - List concrete experiences where you conducted data visualisation and/or used data visualisation tools. | ||||||||||||||||||||
24 | Data Archiving | Archive data | G19 | Archive | I am able to ensure long term preservation of data through archiving | I know how to transfer my work data to a repository for archiving I know archiving centers or warehouses | - Explain why long-term data storage should be provided and how this can be done. - Select from the list of actions those that relate to data archiving. | |||||||||||||||||||
25 | Publication and discoverability | Publish data | H20 | Publish data | I know how to publish my data according to my needs and my discipline I publish my data "as openly as possible, as closed as necessary" and know how to make my results reusable for peers | I respect my institution's data release policy I can assign authorship to a dataset (i.e. "data collected" or co-workers). I am able to indicate all the references associated with the uploaded dataset | - Fill out a quiz in which you point out mistakes in examples of published data and suggest possible improvements. - Showcase previous work experience in publishing data and different tasks involved (respecting the release policies, assigning authorship, indicating references). | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | ||||||||||||||||||
26 | Disseminate data | H21 | Disseminate data | I know how to present my datasets and results in a communicative way understandable to different stakeholders | I am able to produce reports, guides, interpretable files, visuals, ... to explain data management I can share my data and results with a wider audience through popularization activities (e.g. social media posts, workshops, consultations with citizens etc.) | - Generate a visualization of your data set. - Prepare a plan for an information campaign aimed at a wider audience in which you present the results of your project. | Eosc framework, p76 (and next) of https://eoscpilot.eu/sites/default/files/eoscpilot-d7.5-v1.1.pdf | |||||||||||||||||||
27 | Data discoverability | H22 | Discoverability | I am able to improve data discoverability. I am able to ensure interoperability of research results | I can make my dataset interoperable through implementing standards and practices in my discipline. I am able to take advantage of different services that improve data discoverability (PID agencies, aggregators, citation indexes, repositories etc.). I know and can apply Semantic Web standards | - Fill out a quiz about interoperability standards where you identify examples of good practices from a closed list of possible actions. - Prepare a list of examples of different services improving discoverability of data in the context of your discipline. | ||||||||||||||||||||
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