Edits to template

resources/Learning Unit copie/Activities/activity_details_template.md

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+---
+title: "Training Activity Setup"
+author:  "Skills4EOSC T2.3"
+tags: 
+    - FAIR-by-Design Learning Materials
+    - FAIR Learning Objects
+    - Training Activity Template
+---
+
+# Activity name
+This document serves as a guide on how to deliver the specific training activity.
+
+Summary description of the activity.
+
+## Activity duration
+
+## Number of people that it can be performed with
+
+## Goal of activity
+
+## Materials
+- list of materials needed to perform the activity
+- reference digital
+- list physical equipment
+
+## Instructions
+
+Describe how to run the activity. If the activity has several stages break them down into subheadings and provide duration for each.
+
+List any questions the trainer should ask during the activity.
+
+List or link to examples of completing the activity.
+
+
+## Tips and Tricks
+- if the activity can be done both physically and online, provide info on the differences and specifics for each
+
+## Related sources
+- list any additional sources that may be useful for the activity
+
+## Comments
+- how did it go
+- supporting the co-creation process
+- 

resources/Learning Unit copie/Assessment/assessment_template_Lottie_edits.md

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+---
+author: "Skills4EOSC T2.3"
+title: "Assessment Template"
+tags: 
+    - FAIR-by-Design Learning Materials
+    - FAIR Learning Objects
+    - Assessment Template
+---
+
+# Training Assessment Quiz Template
+
+## Quiz Strategy
+
+Define:
+- number of questions
+    - weight per question
+- time limit - quiz duration
+    - or unlimited
+- questions order
+    - as provided
+    - randomly shuffled
+- answers order
+    - as provided
+    - randomly shuffled
+- when it will become available and for how long
+    - open date
+    - close date
+- how many times can it be taken
+    - is there pause between attempts
+    - grading method
+        - highest grade
+        - average grade
+        - first attempt
+        - last attempt
+- what are the completion rules
+    - min % of points acquired (pass mark)
+    - max no. of attempts
+- how the results will be publish
+    - badge or certificate auto-issued
+
+## Questions types and cognitive level
+
+Use the Bloom's taxonomy verbs from the learning objectives to define the questions and their appropriate level of cognitive knowledge. The provided relationship between the Bloom's taxonomy and quiz question type is based on [Reveiu, Adriana. (2019). A Novel Mobile-based Assessment App for Higher Education Setting.](https://www.researchgate.net/publication/344220754_A_Novel_Mobile-based_Assessment_App_for_Higher_Education_Setting):
+1. Remember - recall of information and creation of connections between concepts
+    - multiple answers
+    - single answer
+    - matching
+    - open answer
+    - fill-in-the blank
+2. Understanding - interpret information and retell information in own words
+    - multiple answers
+    - single answer
+    - matching
+    - open answer
+3. Applying - apply to real-world situations, solve problems, complete tasks
+    - multiple answers
+    - open answer
+4. Analyzing - break down information into components and identify relationships and patterns
+    - multiple answers
+    - rank order
+    - matching
+    - open answer
+5. Evaluating - make judgments about quality, accuracy or effectiveness
+    - multiple answers
+    - rank order
+    - open answer
+6. Creating - combine information and create something new
+    - multiple answers
+    - open answer
+
+## Quiz questions types templates
+The GIFT format is one of the easiest for preparation of a list of questions that can be imported into the Moodle question bank and then used to create a quiz.
+
+### Multiple choice question format
+#### Simple format
+Question{= A Correct Answer ~Wrong answer1 ~Wrong answer2 ~Wrong answer3 ~Wrong answer4 }
+#### Multiple choice with multiple right answers
+What two people are entombed in Grant's tomb? {
+   ~%-100%No one
+   ~%50%Grant
+   ~%50%Grant's wife
+   ~%-100%Grant's father
+}
+### True-False
+::TrueStatement about Grant::Grant was buried in a tomb in New York City.{T}
+
+### Short Answer
+Who's buried in Grant's tomb?{=Grant =Ulysses S. Grant =Ulysses Grant}
+
+### Matching
+Match the following countries with their corresponding capitals. {
+   =Canada -> Ottawa
+   =Italy  -> Rome
+   =Japan  -> Tokyo
+   =India  -> New Delhi
+   }
+
+### Missing word
+Moodle costs {~lots of money =nothing ~a small amount} to download from moodle.org.
+
+### Essay
+Write a short biography of Dag Hammarskjöld. {}
+
+## Related material
+- [Moodle Cloze and GIFT Code Generator v4.01](https://hbubecc.wixsite.com/jordan/tools), Excel macro based template for questions definition, updated July 2023
+- maybe you can try [Questgen](https://questgen.ai) and have an AI engine create the questions for you based on the content your will provide

resources/Learning Unit copie/Assessment/quiz_gift_template.txt

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+// Comment lines
+// Quiz questions types templates
+
+// General format
+//::question name::question{answers}
+
+// Multiple choice question format
+// Simple format
+Question{= A Correct Answer ~Wrong answer1 ~Wrong answer2 ~Wrong answer3 ~Wrong answer4 }
+
+// Multiple choice with multiple right answers
+What two people are entombed in Grant's tomb? {
+   ~%-100%No one
+   ~%50%Grant
+   ~%50%Grant's wife
+   ~%-100%Grant's father
+}
+
+// True-False
+::TrueStatement about Grant::Grant was buried in a tomb in New York City.{T}
+
+// Short Answer
+Who's buried in Grant's tomb?{=Grant =Ulysses S. Grant =Ulysses Grant}
+
+// Matching
+Match the following countries with their corresponding capitals. {
+   =Canada -> Ottawa
+   =Italy  -> Rome
+   =Japan  -> Tokyo
+   =India  -> New Delhi
+   }
+
+// Missing word
+Moodle costs {~lots of money =nothing ~a small amount} to download from moodle.org.
+
+// Essay
+Write a short biography of Dag Hammarskjöld. {}

resources/Learning Unit copie/attachments/DMP Lesson Lottie.md

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+metadata?
+---
+
+--- 
+
+```{contents}
+:local:
+```
+
+Adapted from [OpenSciency OpenData] (https://github.com/opensciency/OpenData/blob/main/lessons/lesson5.md)
+# Data Management Plan : Planning for Open Data
+
+	In the previous lessons it has been shown that effective open data needs to be managed. As we have seen this is not trivial and requires work and preparation. Correspondingly, there can be cost implications for your institutions to do this. Rather than facing these issues on an ad hoc basis, one should plan and prepare what you will need to do before you generate the data. With this in mind, we will
+
+* discuss the data life cycle which places a focus on the reuse of data as it is generated.
+* Introduce the concept of a data management plan, where one documents the steps that will be carried out to ensure that your data can be shared in an appropriate fashion.
+* Introduce the concept of metadata, namely documenting your data which is essential if another researcher is to make use of your data.
+* Finally, who to contact in terms of advice and support.
+
+## Learning Objectives
+
+## Target Group 
+- PhD 
+- Early career researchers
+
+This can be summarized in the following image.
+
+![Linear workflow focussed on publications](https://github.com/learnopenscience/TOPS-OC2-data/blob/adb7137694dde403ca54c7b8f755e79dd60fe8d8/assets/Figure5.1.png "Figure 5.1 Linear workflow model")
+
+## Duration
+60 hours
+
+prerequisites: 
+[Lesson 4](https://www.go-fair.org/wp-content/uploads/2022/01/FAIRPrinciples_overview.pdf)
+
+## Learning tools
+- access to training platform 
+- access to word processor
+
+## Planning
+
+### The Data Lifecycle 
+It can be summarised in the following image 
+![FAIR DAta Principles](![](attachments/Pasted%20image%2020231018140834.png)
+Picture 5.1: FAir data principles
+Picture taken from [Ghent University Library](https://www.ugent.be/en/research/datamanagement/after-research/fair-data.htm) 
+
+
+Here the focus is very much moved away from the idea of research -> publication and instead is on the data itself as a first class research output.
+Let’s look at these individual steps
+
+* **Plan**: a description of the data that will be compiled, how the data will be managed and made accessible throughout its lifetime.
+* **Collect**: this corresponds to the data gathering step (illustrated in Figure 5.1).  It can include both primary (raw) and processed data.
+* **Assure**: the quality of the data is assured through checks and inspections.
+* **Describe**: data is accurately and thoroughly described through documentation (e.g. metadata).
+* **Preserve**: these are the steps necessary to make sure that the data will be accessible going forward so in particular ensuring that the data is stored in a fashion that others can use it (in particular storing at a data repository). Ideally this should be done in a fashion that matches the CARE and FAIR principles (lesson 4). This may also include the step of removing data that may not be of use to future researchers. For example, high resolution images may no longer be themselves useful if in the analysis step one has extracted the features of interest from them. Not storing the high resolution image and simply storing the feature data would provide a considerable saving of storage.
+* **Discover**: here other researchers can extract either the entirety or some subset of the data for their own purposes.
+* **Integrate**: data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed (this could include this one data set being analyzed).
+* **Analyze**: corresponds to the data analysis step as illustrated in Figure 5.1.
+There are a variety of different interpretations of the data life-cycle (see the reading list for this lesson) with varying degrees of complexity. It’s also important to note that this is an idealization of what goes in general. Nonetheless, it is important to think of all these steps as an ongoing, interactive process that requires thorough planning and continued consideration and to recognize that they are non-trivial to do.
+
+### 5.2 Data Management Plans (DMP)
+Seeing as the above steps are not trivial before one begins to gather, collate or generate a data set it is useful to plan out what you will do with the data. This is referred to as a Data Management Plan or DMP for short.
+
+A DMP means that you can think ahead of any particular issues that might crop up in terms of handling the data, such as the potential cost of storage, whether data needs to be anonymised and so on.
+
+A detailed description of what one should put into a DMP is described [here](https://the-turing-way.netlify.app/reproducible-research/rdm/rdm-dmp.html) [3]. As outlined in this [document from the UKRI](https://www.ukri.org/councils/stfc/guidance-for-applicants/what-to-include-in-your-proposal/data-management-plan/) [4], the central funder for the UK, these can include answering questions such as
+
+* What type of data will be generated or preserved? This could include data formats, rough estimates of the amount of data to be stored during a research project and similarly what will be preserved beyond the lifetime of the project?
+
+* What type of metadata will be used and preserved. It is worth noting that one of the more detailed aspects of the FAIR principles is to keep the metadata of the data set available even if the original data set no longer exists.
+
+* Where should the data be preserved? i.e. what repository will be used (repositories are discussed in the next lesson). How long should it be stored? (five years? ten years?) More concretely, data regulations can require that certain data be kept in certain ways for at least a certain amount of time. This will vary depending on the type of data (e.g. medical records, population statistics). It is advised that these expiration dates are explored in the literature, and/or policy guidelines.  
+* How will any private data be stored so that it is kept securely?
+
+DMPs are not meant to be exhaustive documents! Typically they are 1-2 pages of A4 and often are less than a few thousand words. The important point is that they sketch out what a researcher or research team plans to do with their data well before they are gathered and can identify any steps that need to be taken rather than facing a major challenge now.
+
+DMPs are [increasingly used by funders](https://dmptool.org/public_templates) and their institutions as a means to have researchers map out what they will do with their data in a research proposal. Research proposals often require DMPs, and hence DMPs are often the ‘sharp end of the stick’ for researchers with respect to Open Science [5].  A good DMP is a criterion for assessment in grant applications and hence doing a good DMP will help your grant be funded.
+
+### 5.3 Documenting your Data (Metadata)
+
+As discussed in the previous lessons, the FAIR principles emphasize the importance of metadata, namely documenting your data. Metadata is described in more detail [here](https://the-turing-way.netlify.app/reproducible-research/rdm/rdm-metadata.html) [6].
+
+A perennial question is what type of metadata and description of the data should be provided for a data set. If you are dealing with electronic data should one provide metadata for a whole set of files, an individual file … each individual bit?
+
+The simplest rule of thumb is if there aren’t any guidelines for your type of data or domain repositories, then try and provide enough documentation about your data that you would ask for if you were downloading this data yourself.
+
+For example if this was data taken from a field trip where location is important then you might want to include longitudinal and latitudinal coordinates. If it’s data from a wet lab then it might include parameters you normally include in the materials and methods section of a paper. If it’s data from purely computational work you may want to list the software run and the parameters used.
+
+Data repositories will be discussed in the next lesson. Domain specific repositories will often give more precise requirements on metadata (another reason to use them).
+
+If there are no guidelines then a simple README file attached with the data is a start (for an example see [here](https://cornell.app.box.com/v/ReadmeTemplate)) - though it’s important to note that ideally one should use metadata schema which is described in much more detail [here](https://www.dcc.ac.uk/guidance/standards) as FAIR data should be machine-actionable [7] [8].
+
+### 5.4 Help
+Much of the ins and outs of dealing with Open Data, or more particularly Open Data that follows good practice such as the FAIR principles, can be technical and lies beyond the domain of knowledge of researchers. How does one navigate this landscape?
+
+This can be summarized in the following diagram -
+
+![Figure 5.3 Diagram pointing to four possible sources of informaiton a researcher can approach.](https://github.com/learnopenscience/TOPS-OC2-data/blob/8509153045f69f2c52c6a6192c52476c54560071/lessons/Figure5.3.png "Figure 5.3 Sources of information and support on Open Data that a researcher could access.")
+
+Figure 5.3 Sources of information and support on Open Data that a researcher could access.
+
+#### **Research communities (international and national)**
+
+Individual research disciplines may already have put together materials and have advice on how to implement Open Science in their discipline. For example [FAIRsharing](https://fairsharing.org/) is a educational and information resource on data and metadata standards [9].  The [Research Data Alliance](https://rd-alliance.org/) have a variety of different [interest and working groups](https://www.rd-alliance.org/groups) in data sharing in specific disciplines. Scientific Societies and Publishers can also provide advice [10] [11].
+
+
+#### **Open Science related communities**
+
+There are a number of communities that are focussed on Open Science activities. [ReproducibiliTea](https://reproducibilitea.org/) is a grass-roots journal club initiative that is based in over 100 institutions and is a forum to discuss reproducibility, closely allied to Open Science [12]. The [FAIRdata forum](https://fairdataforum.org/) allows you to browse materials and raise questions that are related to FAIR [13]. Correspondingly the [PID forum](https://pidforum.org/) allows you to ask questions on PIDs in general [14]. A list of Open Science communities is provided in the next module (Open Tools).
+
+
+#### **Tools and resources**
+
+Finally, there are a range of different tools to help you. For example, [DMPtool](https://dmptool.org/quick_start_guide) and [DMPonline](https://dmponline.dcc.ac.uk/) allow you to build your own DMPs [15] [16]. See the module Open Tools for more details. There are a variety of different catalogs out there one can use to search for materials in this area. [Shanahan, Hoebelheinrich and Whyte](https://www.sciencedirect.com/science/article/pii/S2666389921001720) (2021) have a table of catalogs to search for materials [17].
+
+
+#### **Local library or IT services**
+
+The long term vision is that Higher Education Institutions (HEIs) or Research Performing Organisations (RPOs) [will employ data professionals to advise and support researchers](http://insights.uksg.org/articles/10.1629/uksg.484/) [18]. These individuals have a variety of possible job titles such as Data Librarian, Data Steward, Data Curator and so on. These individuals would advise on aspects on how to make your data adhere to the CARE and FAIR principles, providing appropriate metadata and so on. Some HEIs/RPOs have already made Open Science (or Open Research) policy statements and may not yet have an infrastructure to help but will be interested in supporting you. In some countries there has been progress in this area but it is very early days. Nonetheless, it is worth contacting your University library as they may be able to advise you even on relatively small questions or reque
+Making data open is not trivial. It is not simply a matter of placing a data set onto a cloud drive. Nonetheless, if it is done correctly then the open data is available for reuse. Reuse can be a completely different research team or it could be the same research team that need to carry after a member of the team responsible for the data has moved on. This means one has to think of the data as part of life-cycle and that it is important to make plans (a Data Management Plan) prior to creating the data to ensure that it is stored appropriately. Part of making your data FAIR is provide metadata that describes the data that you are depositing. Finally, do not feel that you have to do all this from scratch. There are a variety of different avenues that you can approach, either on an online basis or sometimes on your own campus.   
+
+## Assessment
+Think about the data sets that were described in lesson 1 as examples of good data.
+
+* Can you identify what were the above steps with that data?
+
+Think now about a data set in your own discipline.
+
+* What would be the steps that you would need to take with that data to match up with the data life cycle?