The aim of this lesson on Open Science is to provide participants with an introduction to the concepts, practices and values lying behind the term 'Open Science'.
-| Duration | Topic | Key points/Teaching Method/Questions | Activities | Resources | -|:------------|:-------------------------|:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------| -| 10 mins | Welcome / Introduction | - housekeeping - introduce trainer and trainees - learning objectives | have your say:This lesson will provide answers to the following questions:
+- What is Open science? (main concepts, principles, key actors) +- What are the key benefits and challenges of open science +- What are the implications of reforming the way research is evaluated? -Note: If the training is relying heavily on presentations then another option for the plan layout is to provided in a slide by slide using slide thumbnails in the topic column. In this case each row in the table should refer to a separate slide in the presentation. - - -### Assessment -- we will use Mentimeter for quizzes during the course -- assessment will be formative and summative -- results will be shown in real time +## Introduction to Open Science +### Definitions of Open Science +### Is Open Science new? +### What's the problem, doctor? -### Certificate or Badge -- no certificate +## Open Science and Research assessment +### How did we get here? +### Things are changing: recent policy evolutions -### Reflection -- how did it go -- highlights: what went great -- improvement points: what went wrong -### Comments -- any additional comments from external parties acting as co-creators \ No newline at end of file diff --git a/resources/Research Data and Open Science/Learning Unit 2/LU2-ResearchData-plan.md b/resources/Research Data and Open Science/Learning Unit 2/LU2-ResearchData-plan.md index eb64e10d..9cc226ae 100644 --- a/resources/Research Data and Open Science/Learning Unit 2/LU2-ResearchData-plan.md +++ b/resources/Research Data and Open Science/Learning Unit 2/LU2-ResearchData-plan.md @@ -11,61 +11,28 @@ tags: - Template --- -# Research Data Lesson plan - -## Research Data, why should we care? -- The aim of this unit is to show participants how research data management is a way to shape integrity and reproducibility of scientific research. A number of concepts are highlighted: such as the research lifecycle, open data and FAIR data. - - (SSH) Research data and Research Data Management definition - - Research Data lifecycle - - Values: integrity and reproducibility - - Open Data / FAIR Data - - Obligations from funding bodies / EC policy - - Open Access - - Examples of open data / public bodies - -### Location -- is it going to be online or physical -- if physical any special room type should be noted (e.g. classroom, IT lab, ...) - -### Total duration - -### Number of attendees -- important to know the max group size -- can be a number range (e.g. 20-30) - -### Learning objectives -After this unit: -- You will be able to indicate what information is considered research data; -- You will be able to explain that the meaning of this research data varies per target group and per phase of the research lifecycle; -- You will understand how open science, data management and FAIR data shape the integrity and reproducibility of scientific research; -- You will have a global overview of the various types of data supporters; -- You will have a broad overview of a number of information sources that can further help you to get up-to-date in data management support. -- learning objectives should be devised using the verbs from the Bloom's Taxonomy -- learning objectives should be SMART - -### Plan -| Duration | Topic | Key points/Teaching Method/Questions | Activities | Resources | -|----------|------------------------|------------------------------------------------------------------------|--------------------------|----------------------------| -| X mins | Welcome / Introduction | - housekeeping - introduce trainer and trainees - learning objectives | ice breaker | e.g. name tags, flip chart | -| X mins | The hook | - focus attention - provide framework | brainstorm expectations | e.g. post-it notes | -| X mins | Training | information + examples | check for understanding | e.g. pptx + handouts | -| X mins | Guided practice | trainer acts as facilitator | exercise or activity | e.g. posters and markers | -| X mins | Summary | key takeaways | reinforcement assignment | e.g. cards | - -Note: If the training is relying heavily on presentations then another option for the plan layout is to provided in a slide by slide using slide thumbnails in the topic column. In this case each row in the table should refer to a separate slide in the presentation. - - -### Assessment -- Mentimeter and online quizzes -- Formative and summative -- results shown during the course in real time -### Certificate or Badge -- no certificate or badge is issued - -### Reflection -- how did it go -- highlights: what went great -- improvement points: what went wrong - -### Comments -- any additional comments from external parties acting as co-creators \ No newline at end of file +# What is Research Data ? +**...and why we should care! ** + +The aim of this unit is to introduce participants to the concepts of research data management (RDM) and FAIR data, showing how they can support integrity and reproducibility in scientific research. The lessons introduces concepts such as research and data lifecycle, open data, FAIR principles for research data, open access.
+ +**Learning objectives** +This lesson will answer the following questions: +- What information is considered research data? (types of data, ways to look at data) +- What are the risks of bad RDM? What are the perks of RDM? +- In what ways are Open Science, RDM and FAIR data enablers of integrity and reproducibility in scientific research? +- What is a data steward? +- How to use a repository? + +## Research Data and RDM +### Core values +### Data jargon +### Data stewards + +## Open Science and Open Access in Practice +### Eu-funded projects +### Open Access, how? +#### Choosing a repository and licensing your work +#### Metadata and persistent identifiers +### Get started with Zenodo +### Here to help: Data Management tools diff --git a/resources/Research Data and Open Science/Learning Unit 2/Teacher/LU2 en plus-Get-started-OS -RDM-content.md b/resources/Research Data and Open Science/Learning Unit 2/Teacher/LU2 en plus-Get-started-OS -RDM-content.md new file mode 100644 index 00000000..1d53280f --- /dev/null +++ b/resources/Research Data and Open Science/Learning Unit 2/Teacher/LU2 en plus-Get-started-OS -RDM-content.md @@ -0,0 +1,28 @@ +--- +title: LU3-GetStarted-content +author: + - Lottie Provost +tags: + - Data + - Repository + - FAIR + - RDM +--- + +# Get started on your OS RDM + +[ List of activities] + +#### **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). +Adapted from [OpenSciency OpenData] (https://github.com/opensciency/OpenData/blob/main/lessons/lesson5.md) + + + + + diff --git a/resources/Research Data and Open Science/Learning Unit 3/LU3-Get-started-OS-RDM-plan.md b/resources/Research Data and Open Science/Learning Unit 2/Teacher/LU2 en plus-Get-started-OS-RDM-plan.md similarity index 100% rename from resources/Research Data and Open Science/Learning Unit 3/LU3-Get-started-OS-RDM-plan.md rename to resources/Research Data and Open Science/Learning Unit 2/Teacher/LU2 en plus-Get-started-OS-RDM-plan.md diff --git a/resources/Research Data and Open Science/Learning Unit 3/.DS_Store b/resources/Research Data and Open Science/Learning Unit 3/.DS_Store deleted file mode 100644 index 4e6d83a9..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/.DS_Store and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/Activities/activity_details_template.md b/resources/Research Data and Open Science/Learning Unit 3/Activities/activity_details_template.md deleted file mode 100644 index 5de0fc03..00000000 --- a/resources/Research Data and Open Science/Learning Unit 3/Activities/activity_details_template.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -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 -- \ No newline at end of file diff --git a/resources/Research Data and Open Science/Learning Unit 3/Assessment/assessment_template_Lottie_edits.md b/resources/Research Data and Open Science/Learning Unit 3/Assessment/assessment_template_Lottie_edits.md deleted file mode 100644 index 1bb11ec7..00000000 --- a/resources/Research Data and Open Science/Learning Unit 3/Assessment/assessment_template_Lottie_edits.md +++ /dev/null @@ -1,106 +0,0 @@ ---- -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 \ No newline at end of file diff --git a/resources/Research Data and Open Science/Learning Unit 3/Assessment/quiz_gift_template.txt b/resources/Research Data and Open Science/Learning Unit 3/Assessment/quiz_gift_template.txt deleted file mode 100644 index 177efa5e..00000000 --- a/resources/Research Data and Open Science/Learning Unit 3/Assessment/quiz_gift_template.txt +++ /dev/null @@ -1,37 +0,0 @@ -// 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. {} diff --git a/resources/Research Data and Open Science/Learning Unit 3/Teacher/LU3-Get-started-OS -RDM-content.md b/resources/Research Data and Open Science/Learning Unit 3/Teacher/LU3-Get-started-OS -RDM-content.md deleted file mode 100644 index 0b3ce2ba..00000000 --- a/resources/Research Data and Open Science/Learning Unit 3/Teacher/LU3-Get-started-OS -RDM-content.md +++ /dev/null @@ -1,173 +0,0 @@ ---- -title: LU3-GetStarted-content -author: - - Lottie Provost -tags: - - Data - - Repository - - FAIR - - RDM ---- - -# Get started on your OS RDM - -[ List of activities] - - -## 1. Title - -### 1.1 subtitle -### 1.2 subtitle - - -## 2. Title -### 1.1 subtitle -### 1.2 subtitle - - -## 3. Title -### 1.1 subtitle -### 1.2 subtitle - - - - - - -#### **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). -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. - - - -### Duration -1 hour - -### 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 -## 1. Planning -#### The data life cycle -With a focus on generating papers, a researcher implicitly ended up with the following research workflow model in mind of how they worked with their data. -It’s important to note here that because the focus is on the paper, there’s no thought to how the data changes at different stages of the process, or thought to how the data should be managed after a paper is published. Usually the data were included as part of the paper as a supplementary file. - -This can be summarized in the following image. (ADD image) - -On the other hand, if one thinks of open data that can be FAIR (and thus reused) then tanother model emerges. In particular, we note that - - Data needs to be available beyond the publication of a paper. - - Data no longer has to be associated with one paper. - - Data can be reanalysed. - - More data, from different sources or the same lab, can be added in at any time, including later. -Instead of the process being a linear progression, with a start and a finish, the process for data becomes more complex and there is cycle. -These ideas were put together in the  - -The original life cycle is complicated but a summary of the life-cycle is listed below - - - -Figure 5.2: A summary of the data life cycle (reproduced from https://old.dataone.org/data-life-cycle) - -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. - -## 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. - -### 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]. - -## 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 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). - -## Conclusion -Rendere i dati aperti non è unèattività banale. Non si tratta semplicemente di collocare un set di dati su un cloud drive. Tuttavia, se viene fatto correttamente, i dati aperti sono disponibili per il riutilizzo. Il riutilizzo può coinvolgere completamente un team di ricerca diverso o potrebbe riguardare lo stesso team di ricerca che deve continuare il lavoro dopo che un membro del team responsabile dei dati si è spostato. Ciò significa che bisogna considerare i dati come parte del ciclo di vita ed è importante pianificare (un Piano di Gestione dei Dati) prima di creare i dati per assicurarsi che siano conservati in modo appropriato. Parte dell'operazione di rendere i tuoi dati FAIR consiste nel fornire metadati che descrivono i dati che stai depositando. Infine, non sentirti obbligato a fare tutto da zero, ci sono vari luoghi dove puoi trovare supporto per rendere i tuoi dati aperti e FAIR. - -## Assessment -Practice what you just learnt - -**Example** -* 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? diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/DMP Lesson Lottie.md b/resources/Research Data and Open Science/Learning Unit 3/attachments/DMP Lesson Lottie.md deleted file mode 100644 index 8625a49a..00000000 --- a/resources/Research Data and Open Science/Learning Unit 3/attachments/DMP Lesson Lottie.md +++ /dev/null @@ -1,130 +0,0 @@ -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. - - - -## 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 - -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 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? diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133218.png b/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133218.png deleted file mode 100644 index 451320a5..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133218.png and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133250.png b/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133250.png deleted file mode 100644 index 451320a5..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/Pasted image 20231018133250.png and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/Skills4EOSC-presentation-template_2023_05.pptx b/resources/Research Data and Open Science/Learning Unit 3/attachments/Skills4EOSC-presentation-template_2023_05.pptx deleted file mode 100644 index 659868f0..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/Skills4EOSC-presentation-template_2023_05.pptx and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/attachments/Pasted image 20231018140834.png b/resources/Research Data and Open Science/Learning Unit 3/attachments/attachments/Pasted image 20231018140834.png deleted file mode 100644 index 4370cccb..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/attachments/Pasted image 20231018140834.png and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/macaw-g8f80c4f64_640.jpg b/resources/Research Data and Open Science/Learning Unit 3/attachments/macaw-g8f80c4f64_640.jpg deleted file mode 100644 index 3796c39c..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/macaw-g8f80c4f64_640.jpg and /dev/null differ diff --git a/resources/Research Data and Open Science/Learning Unit 3/attachments/stone-gda5043761_1280.png b/resources/Research Data and Open Science/Learning Unit 3/attachments/stone-gda5043761_1280.png deleted file mode 100644 index ea6f952f..00000000 Binary files a/resources/Research Data and Open Science/Learning Unit 3/attachments/stone-gda5043761_1280.png and /dev/null differ