section
stringclasses 339
values | question
stringclasses 979
values | answer
stringlengths 4
13.2k
| hash
stringclasses 782
values | source
stringclasses 1
value |
|---|---|---|---|---|
Short-term Data Storage
|
How will the data be stored in the short term?
|
There are many ways to store data in the short term. First, you can choose to store data in physical or logical storage media, such as traditional relational databases: Oracle, MySQL; emerging NoSQL: HBase, Cassandra, Redis and so on. For data that has been centrally pre-processed, it can be stored using memory and the corresponding database can be created to facilitate subsequent management and invocation. In addition, data storage is persistent, which allows data to be kept in a storage medium without being lost due to program exit or computer shutdown. Also, the data storage can be expanded as needed to store large amounts of data and can be expanded as the amount of data increases. In order to ensure the security of data, it can be protected by data encryption, access control, backup and other measures. At the same time, data efficiency is also an important feature of data storage, and data indexing, caching, compression, and other technologies can be used to improve data reading and writing speed and storage efficiency.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Short-term Data Storage
|
a What backup will you have in the in-project period to ensure no data is lost?
|
During the project period, to ensure that we do not lose any data, we will take the following backup measures: 1. Regular Backups: We regularly back up critical data from the project, including documents, codebase, databases, and so on. This ensures that even if something unexpected happens, we are able to recover the data quickly. 2. Multi-location backups: In order to improve the reliability and security of data, we will perform multi-location backups. This way, even if there is a failure or disaster in one location, we can still recover data from other locations.3. 3.Cloud Backup: we will use a reliable cloud service provider to store the backup data. Cloud backup has high availability and flexibility to ensure data security and accessibility. 4. version control: For code and other editable files, we will use a version control system to track and manage changes across versions. This ensures that we can go back to a previous version for recovery if needed. 5. Test Recovery: After backing up data, we will periodically perform a test recovery operation to ensure the integrity and availability of the backup. This helps us to identify and resolve potential problems in a timely manner. Please note that the above backup measures are routine practices to protect the security and reliability of project data. We will adjust and implement them accordingly according to the specific situation and needs of the project. If you have other questions about data backup, please feel free to ask.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Long-term Data Storage
|
How the data will be stored in the long term?
|
There are many different ways to store data for long periods of time, and the following are some common methods: 1. Hard Disk Drive (HDD): A hard disk drive is a common data storage device used to store data on rotating disks. Hard disk drives are typically used to store large amounts of data such as operating systems, applications, and personal files. The advantages of hard disk drives are that they cost less and have a larger storage capacity; the disadvantages are that they are relatively slow and susceptible to mechanical failures. 2. 2. Solid State Drives (SSDs): SSDs use flash memory technology to store data and are faster and more durable than hard disk drives. However, SSDs are relatively expensive and have a small storage capacity. 3. Tape: Tape is an affordable method of data storage that uses magnetic material to store data. Tape is commonly used for backing up and archiving large amounts of data. The advantages of tape are low cost and large storage capacity; the disadvantages are slower access speeds and the need for specialized equipment to read and write data. 4. Cloud storage: Cloud storage is a service that stores data on a remote server over the Internet. The advantage of cloud storage is that data can be accessed anytime, anywhere without worrying about hardware failure; the disadvantage is that it relies on an Internet connection and there may be data security and privacy issues. 5. CD-ROM: CD-ROM is an optical data storage medium that can store data such as audio, video and software. The advantages of CD- ROMs are that they are inexpensive and easy to distribute; the disadvantages are that they have limited capacity and are easily damaged.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Long-term Data Storage
|
a. Where have you decided to store it, why is this appropriate?
|
The following factors need to be considered when choosing the method and location of long-term data storage: 1. Volume of data: Select the appropriate storage device or service based on the amount of data to be stored. 2. Frequency of access: If the data needs to be accessed frequently, choose a faster storage device or service. 3. Cost: Choose the right storage device or service according to your budget. 4. Security: For sensitive data, you need to consider data security and privacy issues and choose a reliable storage device or service. 5. Scalability: If the amount of data is expected to grow in the future, you need to choose a storage device or service with scalability.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Long-term Data Storage
|
b. How long will it be stored for and why?
|
The duration of storage of project data depends on a number of factors, such as the type of data, the value of the data, the security of the data and the integrity of the data. Generally speaking, both backup and archiving belong to the data preservation stage, archiving in preserving data for a long time, and backup in preserving dynamic data for disaster recovery. For some data with important value, such as those in the medical and financial fields, long-term preservation may be required. However, long-term storage is not only costly but also difficult to manage due to changes in data formats and loss of physical storage media. For example, digital film and television have even shorter migration cycles, with Warner Bros. aggressively migrating data every three years because hard drives can wear out in three to five years. In the area of personal information protection, there is some tension between the requirement that data be kept for a minimum period of time (i.e., deleted as soon as possible) and the legal requirement that relevant data (containing personal information) be retained for at least a certain period of time for regulatory and other purposes. For example, for a project with a large volume of data, tens of millions of pieces of data are entered into the database every day and it is fixed that no data will be retained in the database for 23 hours.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Long-term Data Storage
|
c. Costs of storage – why are these appropriate? Costs related to long term storage will be permitted providing theseare fully justified and relate to the project Full justification must be provided in Justification of Resources (JoR)
|
Storage costs are the total costs incurred in maintaining inventory, and these costs include the cost of keeping the physical items and the cost of the capital tied up in the inventory itself. Specifically, storage costs are divided into two parts: fixed costs and variable costs: 1. Fixed storage costs: independent of the amount of inventory stored, and independent of the amount of inventory and the length of storage time. For example, warehouse depreciation, warehouse custodian fixed monthly wages. 2. Variable warehousing costs: the number of inventories in proportion to the number of inventories, the more the number of inventories, the higher the variable costs. For example, the interest cost of funds occupied by inventory, inventory insurance premiums, inventory salvage deterioration losses. When considering the costs associated with long-term storage, it is important to ensure that these costs are fully justified and directly related to the project. This means that these costs need to be fully justified in the Justification of Resources (JoR) and demonstrate that they are indeed necessary for the project and compare favorably with other potential options. In addition, capital costs need to be considered, as storage costs include not only the cost of physical storage, but also the cost of the capital used.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
How the data will be shared and the value it will have to others
|
Project data can be shared in a number of ways, including: 1. Public release: Uploading data to a public database or website for anyone to access and use. 2. Data exchange: Exchanging data with other organizations or individuals to access the data they have. 3. Data licensing: Licensing data to other organizations or individuals, usually for a fee. 4. Data Sharing Agreement: Entering into a data sharing agreement with another organization or individual that specifies the responsibilities and obligations of both parties in relation to the use and sharing of data. The value of these data to others is mainly in the following areas: 1. Scientific research: Researchers can utilize the data for various scientific studies, such as climate change, ecosystem restoration and urban planning. 2. Policy making: Government departments can make more scientific and effective policies and plans based on these data. 3. Business decision-making: Enterprises can utilize these data for market analysis, competitive analysis, risk assessment, etc., so as to make smarter business decisions. 4. Education and training: Educational institutions can utilize these data to provide students with practical teaching resources and improve teaching quality. 5. Public Participation: The public can access these data to understand the progress and results of the project and improve public participation and satisfaction.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
a. How the data will enhance the area and how it could be used in the future?
|
The main ways in which these data will enhance the value of the region are as follows: 1. Promoting sustainable development: through the analysis and utilization of project data, the sustainability of projects can be better assessed and monitored, thus ensuring a balanced development of projects in economic, social and environmental terms. 2. Improving resource utilization efficiency: Project data can help identify and optimize resource allocation, improve resource utilization efficiency and reduce waste. 3. Promote innovation and technological development: Project data can provide valuable information and insights for technological innovation and development, and promote regional technological progress and industrial upgrading. 4. Improve government governance: Project data can provide governments with real-time information on project progress, effects and problems, helping them to better manage projects and make decisions. 5. Enhance public trust and support: Through open and transparent data sharing, public trust and support for the project can be increased, creating a favorable social environment for the smooth implementation of the project.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
b. Releasing the data – advise when you will be releasing and justify if not releasing in line with AHRC guidelines of aminimum of three years. If the data will have value to different audiences, how these groups will be informed?
|
1. Data sensitivity: If the data relate to the privacy of individuals or sensitive information, additional protective measures may be required, which may result in a different timetable for releasing the data than the standardized guidelines. In such cases, a detailed explanation should be provided as to why a different timetable is required and what measures have been taken to protect the security and privacy of the data. 2. Emergencies: In certain emergency situations, such as public health crises, natural disasters or other events requiring an immediate response, there may be a need to release data quickly to support decision-making and action. In such cases, the nature of the emergency and why the data release needs to be prioritized should be described. 3. Legal or regulatory requirements: There may be specific legal or regulatory requirements that may require the release of data within a specific time frame. In this case, details of the relevant legal or regulatory requirements should be provided and an explanation given as to why these requirements result in a change in the release schedule. 4. Data timeliness: If the data is highly time-sensitive, such as financial market data or weather forecasts, it may be necessary to release the data in real time or near real time to meet the needs of the audience. In this case, the timeliness of the data and why a faster release schedule is needed should be explained. To ensure that information is accessible to different audiences, the following steps can be taken: - Publicize data releases through official websites, press releases, social media, and other communication channels. - Work with partners, stakeholders and key influencers to ensure they are aware of when and how the data will be released. - If possible, make data available in multiple formats (e.g., spreadsheets, databases, visual charts, etc.) so that different audiences can access and use the data according to their needs. - For sensitive or restricted data, consider setting up access controls or a request process to ensure that only the right people have access to the data. - If there are multiple datasets or reports being published at the same time, a unified platform or page can be created to make it easy for audiences to find and access all relevant information.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
c. Will the data need to be updated? Include future plans for updating if this is the case.
|
Whether project data need to be updated depends on the nature and objectives of the project. Updates are necessary if the project involves real-time data or data that need to reflect the latest situation. Future update plans should include the frequency and methodology for updating data on a regular basis to ensure timeliness and accuracy.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
d. Will the data be open or will you charge for it? Justify if charging to access the data
|
Data can be either open or fee-based. Open data means that anyone is free to access and use the data, whereas data for which a fee is charged requires payment of a fee to access it. If a fee is charged for accessing data, it needs to be justified. This can be done in the following ways: 1. the cost of data collection and maintenance: if the cost of data collection and maintenance is high, e.g. complex data collection, cleansing and organizing work is required, then it is reasonable to charge a fee. 2. data quality and exclusivity: if the data is of high quality and exclusivity, i.e., the data is from a reliable and verified source and only a specific organization or individual owns the data, then charging a fee ensures the exclusivity and value of the data. 3. Data protection and privacy: If the data contains sensitive information or involves personal privacy, then charging a fee can provide better data protection measures to ensure data security and privacy.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Data Sharing
|
e. Financial requirements of sharing – include full justification in the JoR
|
Shared financial requirements need to be fully justified in JoR. This may include the following: 1. Financial support: Sharing data may require additional funds to support the collection, organization, storage and maintenance of the data, etc. These funds may be used to purchase equipment, hire professionals, conduct data analysis, etc. 2. Fostering collaboration and innovation: Sharing data can foster collaboration and innovation. By making data available to other organizations or individuals, new ideas can be stimulated, new solutions can be discovered, and projects can be driven forward. 3. Public interest: Sharing data may have a positive impact on the public interest, e.g. improving social services, enhancing efficiency in decision-making, and promoting scientific research. In this case, the financial requirements of shared data can be seen as a contribution to society. In conclusion, the need for project data to be updated and the openness and chargeability of the data need to be determined on a case-by-case basis. Financial requirements for shared data need to be supported by a strong rationale, taking into account factors such as financial support, collaborative innovation and public interest.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Ethical and Legal Considerations
|
a. Any legal and ethical considerations of collecting the data
|
There are certain ethical and legal requirements that do need to be followed when conducting data collection to ensure the legitimacy and sustainability of data analysis. Here are some key legal and ethical considerations: 1. **Laws and regulations**: Countries and regions have different laws and regulations with strict rules on data collection, use, sharing and protection. For example, China has clearly stipulated the laws and regulations that should be complied with when carrying out data processing activities, such as respecting social morality and ethics, complying with business and professional ethics, being honest and trustworthy, fulfilling the obligation of data security protection, and assuming social responsibility. In addition, in order to cope with the arrival of the big data era, China is also actively promoting the legal infrastructure and supporting construction at the level of personal data rights. 2. **Rights and interests of data stakeholders**: "My data, my master" is an ideal state. This means that from the collection, storage and transmission of data to its use and destruction, data stakeholders should be involved and protected by laws and regulations. In particular, when collecting and using personal data, although it is necessary for the public interest, it is also necessary to adhere to the lawful and reasonable application to ensure the security of personal data and privacy rights. 3. **Industry self-regulation and international cooperation**: Relevant industry organizations should, in accordance with their statutes, formulate data security codes of conduct and group standards in accordance with the law, strengthen industry self- regulation, and guide their members to strengthen data security protection. At the same time, the State also actively carries out international exchanges and cooperation in the fields of data security governance, data exploitation and utilization, and participates in the formulation of international rules and standards related to data security. To summarize, data collection is not only a technical issue, but also a complex issue involving many aspects of law, ethics and society. When data collection is carried out, these factors must be considered comprehensively to ensure the legality, security and morality of the data.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
Ethical and Legal Considerations
|
b. Legal and ethical considerations around releasing and storing the data – anonymity of any participants, followingpromises made to participants
|
The legal and ethical issues involved in publishing and storing data are quite extensive. Considered from a legal perspective, the Data Security Law of the People's Republic of China stipulates the legal requirements to be observed in carrying out data processing activities. This includes respecting social morality and ethics, complying with business ethics and professional ethics, being honest and trustworthy, fulfilling data security protection obligations, and assuming social responsibility. In addition, where laws and administrative regulations stipulate the purpose and scope of data collection and use, data shall be collected and used within the purpose and scope stipulated in the laws and administrative regulations. In terms of ethics, respect for personal privacy is the most basic requirement. Private information of others should not be collected, used or disclosed without authorization or legitimate reasons. This not only helps to safeguard an individual's right to privacy, but also helps to build public trust in technology and data. In addition to the above legal provisions and ethical principles, there are also industry organizations that, in accordance with their charters, formulate data security codes of conduct and group standards in accordance with the law, strengthen industry self- regulation, and guide their members to strengthen data security protection. At the same time, the State has actively engaged in international exchanges and cooperation in the areas of data security governance and data exploitation and utilization, and has participated in the formulation of international rules and standards related to data security.
|
17e480756035d6484b0b2af7e92f9230
|
dmponline.dcc.ac.uk
|
DATA DESCRIPTION AND COLLECTION OR RE-USE OF EXISTING DATA
|
How will new data be collected or produced and/or how will existing data be reused?
|
1. Using a data collector to collect data 2. Perform visual analysis on CNKI and create corresponding icons 3. Using Vos for Common Analysis of Keywords 4. Search for relevant articles on CNKI and Baidu, extract relevant data for re summarization and analysis
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
DATA DESCRIPTION AND COLLECTION OR RE-USE OF EXISTING DATA
|
What data will be collected or produced?
|
1. Collect the spatial distribution of coal 2. Collect data on coal safety accidents in the past decade
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
DOCUMENTATION AND DATA QUALITY
|
What metadata and documentation will accompany the data?
|
Most of the data we use comes from CNKI. PDF grid available. There are also pictures available. The document is in the form of a PPT
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
DOCUMENTATION AND DATA QUALITY
|
What data quality control measures will be used?
|
We use manual analysis and data-driven analysis to ensure the accuracy of the data. We also conduct standardized data collection through repeated sampling and mathematical model validation
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
STORAGE AND BACKUP DURING THE RESEARCH PROCESS
|
How will data and metadata be stored and backed up during the research?
|
Mainly stored through USB flash drives, as well as laptops of relevant personnel Five members in the group, each with a copy to avoid loss
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
STORAGE AND BACKUP DURING THE RESEARCH PROCESS
|
How will data security and protection of sensitive data be taken care of during the research?
|
The analysts were assigned to the group for the second time, so there weren't too many people spying, so there wasn't very strict protection in place
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
LEGAL AND ETHICAL REQUIREMENTS, CODES OF CONDUCT
|
If personal data are processed, how will compliance with legislation on personal data and security be ensured?
|
We will protect our data in accordance with relevant laws, and also annotate the data we cite from others to avoid plagiarism or infringement of the rights of data owners
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
LEGAL AND ETHICAL REQUIREMENTS, CODES OF CONDUCT
|
How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation isapplicable?
|
Two other team members are responsible for collecting my own data this time, and I have been authorized to avoid any related disputes
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
LEGAL AND ETHICAL REQUIREMENTS, CODES OF CONDUCT
|
What ethical issues and codes of conduct are there, and how will they be considered?
|
This project was led by the team leader, who then assigned tasks for processing. Everyone played their respective roles and also shared relevant data
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
DATA MANAGEMENT RESPONSIBILITIES AND RESOURCES
|
Who will be responsible for data management?
|
The management of this data is handled by the team leader Han Jingyang
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
DATA MANAGEMENT RESPONSIBILITIES AND RESOURCES
|
What resources will be dedicated to data management and ensuring that data will be FAIR?
|
The charts and visualized stages will be used for data management, and everyone can view them, but editing is prohibite
|
10076d40450017fbc1b024d228f7bdb7
|
dmponline.dcc.ac.uk
|
Data Collection
|
What data will you collect or create?
|
The data I will collect is the data on graduate school entrance examination and employment in the past five years, as well as the views and attitudes of college students in the new era on graduate school entrance examination and employment, and I will also collect the views of people other than college students on graduate school entrance examination and employment to form a comparison, and then comprehensively find out the tendency of college students to enter graduate school and employment. The format I chose was both text and binary, and the data could be shared and accessed for long periods of time, as well as existing data that could be reused.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Data Collection
|
How will the data be collected or created?
|
When collecting data, I will use anonymous questionnaires, offline interviews and online searches to collect data, and the data collection will be random and anonymous. In building data folders, the collected data at different stages is classified according to different age groups, and then put them in different folders, and then the files in different folders are named again according to the collection time and age group. When it comes to handling version control, I divide the root group members and make it clear to each member. In terms of ensuring my process and quality, I will hold regular progress exchange meetings with the team members to present and communicate the results of our current work, and then implement the next steps and determine the quality of our various stages.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
My data will be accompanied by an analysis of the graduate school entrance examination fever and employment fever in recent years, as well as a document on the opinions of different people on the graduate school entrance examination and employment. I also trace the data in some documents, indicating their source and related information. For the capture of document data, I will start from different aspects, organize the different data obtained, and mark the first content of the metadata
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
When acquiring some data, I will first seek the unification of the relevant responsible parties, and then store the obtained data after consent. In terms of the protection of participants, we will anonymize the data knocked down by the opponent, and protect personal information when it is disclosed or displayed to the public. The data is also processed at the time of use to ensure that the information of the participants is not leaked. When storing some data securely, we will choose a more secure method, such as a dedicated computer and network, and back up our data. When transmitting, use a USB flash drive for transmission, and if you want to transfer remotely, send it directly to the other party's mailbox.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage copyright and Intellectual Property Rights (IPR) issues?
|
For the data we collect, only the people involved in our project can get the data, and for some of the more sensitive data, only the main managers of the project can have it. For some data that can be reused, we indicate and annotate it. When third parties use our duplicate data, we limit the number of times it is used, etc. Data sharing will not be postponed, and the data sharing phase will take place in real time
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will the data be stored and backed up during the research?
|
For the backup and storage of data, we will store and back up data based on sufficient space, and there will be no additional service fees. When backing up data, we will back up through different computers, as well as different USB flash drives, each person's computer backs up part of the data, and everyone's U disk backs up the complete data, so that you can protect the data well when backing up the data.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will you manage access and security?
|
Data may face the risk of theft and loss, for the risk of theft, we take a more rigorous backup and storage method to save our data, when someone needs our data needs to apply to us, and when we agree, we will give others the corresponding required data. Of course, we will also increase our motivation to protect our data from this risk. When our data is accessed, the system will prompt us and record the IP address of the other party, so that our data can be safely accessed. If data is created or collected on-site, we secure the transmission of our data using our own "secure channels" to ensure that it is securely transmitted to our main security systems
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
Which data are of long-term value and should be retained, shared, and/or preserved?
|
For contractual, legal, or regulatory purposes, we destroy some of the participant's personal data, and only retain the data that is useful for our project (and data that can be made public is also processed). We make choices about the data we collect according to the needs of our project, and use and protect it according to the importance of it. These data are still very useful in the study of the happiness index of our college students and different groups of people. The retention and retention of our data will last for a period of six months.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
For our data, we store it in the DMP. We don't charge any fees for our data. We also check the data regularly, once every half a month.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Data Sharing
|
How will you share the data?
|
Potential users can access the application data through us. Our data is available to the general public, and we share it when necessary for the general public. We will do so by storing and sharing data. The provision of data requires our consent and is given for a certain period of time. I don't want to get persistent identifiers for our data.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Data Sharing
|
Are any restrictions on data sharing required?
|
We will restrict access to our data by a certain amount, but for reducing the limit, our current decision is to establish a list of trusted persons and reduce the restriction on it. We need to use the data exclusively for a period of one month, and we want to ensure the authenticity and integrity of our data, and only when it is fully processed can it be completely disclosed or accessed. We need a data sharing agreement.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
Our project administrators need to manage and be responsible for the data and review and modify it at the same time, specifically Luo Yukuan and He I for data management, and Luo Yukuan for data review and revision. The two of us will be responsible for every data management activity. In collaborative research projects, I assign partners according to their abilities and personalities to ensure that our projects are run well. RDM's data ownership and responsibilities become part of any consortium agreement or contract between partners.
|
da3d6689661ebbccbf432a1304c6da19
|
dmponline.dcc.ac.uk
|
Data Collection
|
What data will you collect or create?
|
Collect the current status of smart pension on China's Baidu website, CNKI, etc
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Data Collection
|
How will the data be collected or created?
|
Collect the trend analysis of smart elderly care on websites such as CNKI and China Weibo
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
Analysis of the current situation and development trend of smart pension.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
Discuss anonymous and non-anonymous options with participants, make sure all the pros and cons are covered, and then let them decide for themselves. In conclusion, it is necessary for us to seek better ways to treat participants in our research, including: 1. replacing "informed consent" with the power to assess risk; 2. Remuneration for the time and expertise that participants contribute to the research; 3. Support participants to make their own decisions about whether to be anonymous or not.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage copyright and Intellectual Property Rights (IPR) issues?
|
This is achieved through the use of strong passwords, multi-factor authentication, permission management, and audit logs. For sensitive data, more stringent access control policies can be adopted, such as fine-grained permission control using access control lists (ACLs) or access control matrices (ACMs). 2) Classify and label data.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will the data be stored and backed up during the research?
|
(1) Back up important files to the personal network disk (NAS) allocated in the group through FreeFileSync, and automatically check the update synchronization, method: Research Data Backup and Synchronization Solution (FreeFileSync) - Dorad's Articles - Zhihu zhuanlan.zhihu.com/p/11 (2) Upload some files that need to be shared to the shared disk (NAS) in the group in time, and update the things in the personal disk to the backup disk (NAS) regularly;In the event of an accident, the data can be used in the network disk for backup.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will you manage access and security?
|
Manage access and security with access control, identity authentication, data confidentiality, and disaster recovery.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
Which data are of long-term value and should be retained, shared, and/or preserved?
|
The current situation and development trend of smart pension have long-term value and should be retained.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
It is stored in the network disk and USB flash drive, as well as in the data backup of the computer.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Data Sharing
|
How will you share the data?
|
Publish the data and research analysis results on websites such as CNKI.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
Zhuyunhui
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
At present, the wisdom of the elderly is still in the development stage, after the development of science and technology, the wisdom of the elderly will make the elderly life more comfortable and happy.
|
1c7a5f278dd8bfe22f91767f31d37e67
|
dmponline.dcc.ac.uk
|
Data Summary
|
Will you re-use any existing data and what will you re-use it for?
|
No, I will not re-use any existing "raw" data from other repositories.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the purpose of the data generation or re-use and its relation to the objectives of the project?
|
Data generation is associated with the work packages as follows: WP1: Engage teachers in South Tyrol and Veneto via PAR in the ethnographic exploration of their multilingual classrooms over one academic year. The four phases of participatory action research (PAR) used in STEMCo (observation, reflection, planning, implementation) are associated with different types of data. In the observation and implementation stages, Type A data and associated Type D data are collected via participant observation in classrooms and schools. These are then used in the generation of Type C data. In the reflection and planning stages, pseudonymized Type A-C-D data are shared with teachers at the school where the data were collected in order to stimulate reflection on teacher practice and plan potential modifications to teaching methods, approaches, classroom management, and interactional elements of classroom life. Type C data is then generated based on the recordings of workshop sessions with teachers. WP2: Analyze classroom discourse, didactics, artifacts, and PAR interviews and workshops to identify communicative patterns in these classrooms and to develop pedagogical strategies for teaching multilingual students. The data generation phase of STEMCo (WP1) has the purpose of involving teachers in the exploration of discourse in their own schools (WP2). In combination, WP1 and WP2 facilitate a participatory research approach that involves teachers in reflection on the ways they and their students communicate, and provides opportunities for them to learn from themselves and their colleagues via linguistic ethnographic methods. The reflections and modifications have a cumulative effect over the course of the academic year, facilitating not only a means of managing and accommodating the linguistic diversity of teachers' current classes, but also a means of doing the same in the future. WP2 also includes the analysis of data by the research team with the aim of making scientific contributions to empirical and theoretical studies in the linguistic anthropology of education and associated disciplinary areas. WP3: Exploit findings from WP1 and WP2 to design, promote, and launch open-access, research-driven and policy- informed teacher education materials for teachers who (will) work in multilingual communities in Europe. After data collection and subsequent engagement with the fieldsites is complete, WP3 involves the creation of teacher training materials for pre-service and in-service teachers working in linguistically diverse settings. This phase draws on pseudonymized and, when possible, anonymized Type A-B-C-D-E data in the generation of Open Access materials and courses, and will be facilitated by short visits and a secondment with collaborating research centers and teacher training programs. Courses and repositories of materials for teachers working with multilingual students and/or in linguistically diverse settings will be made available in an Open Access format that is yet to be determined, but may resemble the mode of presentation used in learninghowtolookandlisten.com. Pseudonymized or anonymized audio, video, and transcript data will be available in Open Access format for researchers in linguistics and education via the TalkBank repository (talkbank.org). Metadata will be generated for all Type C and E data and will be made available on CLARIN.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the expected size of the data that you intend to generate or re-use?
|
I intend to record approximately 10-15 hours of discourse per week (both in the form of classroom discourse and interviews). Over the course of the 2022-2023 academic year, this amounts to approximately 350-500 hours of recordings. Type A & B data will be reviewed and matched up with one another so that classroom events and interviews can be coded qualitatively, compared with Type D data, and then transcribed to produce Type C data. It is unlikely that all recorded discourse will be transcribed over the course of the project. Instead, the researcher will draw on personal observations of the day's events, compare to fieldnotes, and use the fieldnotes as a guide to identify moments of talk that fall within the scope of the project. These moments will be transcribed and will then be coded further so as to identify themes to pursue in analysis. Type E data will be collected via 2-3 short surveys of maximum 5 questions each from approximately 90-100 participants. REV 29.06.2023: To date, I have 1.3TB of data stored. This will fluctuate as originals are transformed into pseudonymized/anonymized copies.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the origin/provenance of the data, either generated or re-used?
|
The discourse data was obtained via recordings of classes in session and interviews with teachers at two middle schools. Transcripts and codes will be produced by the researcher and other team members. Surveys were gathered from student participants.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data Summary
|
To whom might your data be useful ('data utility'), outside your project?
|
The data will be useful to two groups of users: 1. Applied linguists (for discourse analysis, interactional sociolinguistics, second language development, etc.), linguistic anthropologists (enregisterment, speech chains, etc.), anthropologists of education (academic discourse socialization, student identity development, etc.) 2. Teachers and teacher trainers (in pre-service teacher education courses at universities, in professional development initiatives at schools and school boards for in-service teachers, for practitioner-researchers in their own classrooms)
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will data be identified by a persistent identifier?
|
Each transcript and media file in TalkBank is assigned a Permanent ID via the Handle System (www.handle.net), and each corpus has an ISBN and DOI (digital object identifier) number. Data deposited in CLARIN will be assigned handles.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will rich metadata be provided to allow discovery?What metadata will be created? What disciplinary or general standards will be followed? In case metadata standardsdo not exist in your discipline, please outline what type of metadata will be created and how.
|
Rich metadata will be provided to allow for discovery of Type A-B-C-E data. The DDI standards will be followed in order to maximize data discoverability (https://ddialliance.org/). LRMI schemas will be followed in the labeling of data archived in TalkBank (https://www.dublincore.org/specifications/lrmi/concept_schemes/). TEI standards for ensuring findability of texts deriving from transcribed speech will be followed wherever possible for Type C data (https://tei-c.org/release/doc/tei-p5-doc/en/html/TS.html). The vocabulary used for creating metadata descriptions will follow CESSDA standards (https://www.cessda.eu/Tools/Vocabulary-Service).
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will search keywords be provided in the metadata tooptimize the possibility for discovery and then potential re-use?
|
The indexing and registration of materials in TalkBank have been adapted to align with OLAC (Online Language Archives Community) at www.language-archives.org and VLO (Virtual Language Observatory) at https://vlo.clarin.eu . As is stated on the TalkBank website, "These systems allow researchers to search for whole corpora or single files, using terms such as Cantonese, video, gesture, or aphasia. In order to publish or register TalkBank data within these systems, we create a 0metadata.cdc file at the top level of each corpus in TalkBank. Some of the fields in this metadata file are designed for indexing in OLAC and some are designed for the CMDI system used by VLO and the related facility called The Language Archive (tla.mpi.nl). Because of the highly specific nature of the terms and the software used for regular harvesting and publication of these data, we do not require users to create the 0metadata.cdc files. The following table explains what keywords are expected within each field of these files. The first fields listed are for OLAC and the later ones are for CMDI. For CMDI, the values unknown and unspecified are also available for most of the fields. ... We use a CLAN program that takes the information from the 0metadata.cdc files and from the header lines in each transcript." For more information about the specific fields, please see the table in Section 6.2: https://talkbank.org/manuals/CHAT.html#_Toc107417263
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will metadata be offered in such a way that it can beharvested and indexed?
|
Please see above.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Will the data be deposited in a trusted repository?
|
Yes, data will be deposited in TalkBank's repositories ClassBank and/or BilingBank, as well as in the Eurac node of CLARIN.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Have you explored appropriate arrangements with the identified repositorywhere your data will be deposited?
|
For TalkBank, STEMCo audiovisual and transcript data has been provisionally accepted. The final decision will be made on the basis of the filters used for pseudonymization of audiovisual materials, since TalkBank does not typically accept "doctored" audiovisual materials. In the event that I am not able to deposit edited video recordings in TalkBank, I will either (a) submit only transcripts and accompanying audio data or (b) submit transcripts, accompanying audio data, and a visual representation (a diagram) of the seating arrangement during the speech event. The Eurac node of CLARIN does not currently host audiovisual files. I will therefore only deposit textual data and survey results (Types C, D, E).
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Does the repository ensure that the data is assigned an identifier? Will therepository resolve the identifier to a digital object?
|
Yes. See above.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Other research outputs
|
In addition to the management of data, beneficiaries should also consider and plan for the management of otherresearch outputs that may be generated or re-used throughout their projects. Such outputs can be either digital (e.g.software, workflows, protocols, models, etc.) or physical (e.g. new materials, antibodies, reagents, samples, etc.).
|
Other research outputs in addition to data will include teacher training materials in the form of an open access teacher training course (or a series of independent modules), as well as digital materials that can be used online or printed out and used in class.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Other research outputs
|
Beneficiaries should consider which of the questions pertaining to FAIR data above, can apply to the management ofother research outputs, and should strive to provide sufficient detail on how their research outputs will be managedand shared, or made available for re-use, in line with the FAIR principles.
|
Open access publications will be indexed with appropriate metadata based on the publisher's conventions, with persistent identifiers, and will be findable via Google Scholar, university library searches, and other conventional means of accessing research materials. Teacher materials will be made available at an accessible URL (no password, no email address, no login information required). In order to avoid any type of membership, registration, or subscription, the costs of hosting the materials for at least ten years will be paid up front in order to guarantee their availability even without a DOI. Once the materials have been created and are in the piloting phase, the PI will seek out a permanent "home" for them (e.g., European Centre for Modern Languages, a university, Eurac Research, or another body of the European Commission). This, however, cannot be determined until the final product of STEMCo has been drafted.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
What will the costs be for making data or other research outputs FAIR in your project (e.g. direct and indirect costsrelated to storage, archiving, re-use, security, etc.) ?
|
There is no direct cost for hosting data on TalkBank or the Eurac node of CLARIN. Open Access publications that come out of STEMCo will likely cost approximately €2500-3000 each, and there may be as many as five or six of these. Some will be covered by the grant awarded by the European Commission and others of these costs will be covered by Eurac Research. I anticipate hiring an external collaborator to assist with metadata creation and other technical aspects of data management for approximately €8000-10.000 total. Hosting the teacher materials in an entirely open format for at least 10 years will also likely cost approximately €1500-3000. I anticipate spending approximately 3-6 person months on various aspects of the FAIRification of data for my project.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
How will these be covered? Note that costs related to research data/output management are eligible as part of theHorizon Europe grant (if compliant with the Grant Agreement conditions)
|
These costs will be covered either by the MSCA grant itself, by the host institute (Eurac Rsearch), or by other funds made available by third parties.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
Who will be responsible for data management in your project?
|
The project leader/PI, Andrea Leone-Pizzighella, is responsible for data management during the project. TalkBank and CLARIN are responsible for long-term preservation once the data from STEMCo have been accepted.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
How will long term preservation be ensured? Discuss the necessary resources to accomplish this (costs and potentialvalue, who decides and how, what data will be kept and for how long)?
|
Long-term data and metadata preservation for TalkBank is guaranteed by Carnegie Mellon University's KiltHub, as described here: https://www.talkbank.org/info/CMU_Support.pdf The Eurac node of CLARIN is linked to the broader CLARIN network which ensures long-term preservation of data and metadata should the Eurac node close. After the 36 person months of the project have ended, the original data will have already been destroyed, and only the pseudonymized and anonymized data will be retained (in the above two repositories and the teacher training website).
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data security
|
What provisions are or will be in place for data security (including data recovery as well as secure storage/archivingand transfer of sensitive data)?
|
All data will also be stored in the OneDrive/Sharepoint at Eurac Research. The folders on the PI's desktop and on the server will be encrypted, and the passwords for encryption management will be stored securely by the PI and by the ICT department of Eurac. All files on the PI's desktop are automatically backed up to Eurac's secure server as soon as the computer is connected to the internet and the VPN. Eurac is ISO certified -- Quality Management System (9001) and Information Security Management (27001)
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Ethics
|
Are there, or could there be, any ethics or legal issues that can have an impact on data sharing? These can also bediscussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapterin the Description of the Action (DoA).
|
The raw discourse data (audio, video) cannot be shared because the ethnographic nature of the project may reveal sensitive personal data about participants. The survey data that is made publicly available will only be published in aggregated form. Selections of audio and video, pending the functionality of audiovisual filters, may be made available in a public repository. Transcripts of discourse data will be pseudonymized and made publicly available in excerpted form so as to ensure that no sensitive data is shared.
|
cd30b5e64cf7d9bebd270153d708f336
|
dmponline.dcc.ac.uk
|
Data Summary
|
Will you re-use any existing data and what will you re-use it for?
|
No, I will not re-use any existing "raw" data from other repositories.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the purpose of the data generation or re-use and its relation to the objectives of the project?
|
Data generation is associated with the work packages as follows: WP1: Engage teachers in South Tyrol and Veneto via PAR in the ethnographic exploration of their multilingual classrooms over one academic year. The four phases of participatory action research (PAR) used in STEMCo (observation, reflection, planning, implementation) are associated with different types of data. In the observation and implementation stages, Type A data and associated Type D data are collected via participant observation in classrooms and schools. These are then used in the generation of Type C data. In the reflection and planning stages, pseudonymized Type A-C-D data are shared with teachers at the school where the data were collected in order to stimulate reflection on teacher practice and plan potential modifications to teaching methods, approaches, classroom management, and interactional elements of classroom life. Type C data is then generated based on the recordings of workshop sessions with teachers. WP2: Analyze classroom discourse, didactics, artifacts, and PAR interviews and workshops to identify communicative patterns in these classrooms and to develop pedagogical strategies for teaching multilingual students. The data generation phase of STEMCo (WP1) has the purpose of involving teachers in the exploration of discourse in their own schools (WP2). In combination, WP1 and WP2 facilitate a participatory research approach that involves teachers in reflection on the ways they and their students communicate, and provides opportunities for them to learn from themselves and their colleagues via linguistic ethnographic methods. The reflections and modifications have a cumulative effect over the course of the academic year, facilitating not only a means of managing and accommodating the linguistic diversity of teachers' current classes, but also a means of doing the same in the future. WP2 also includes the analysis of data by the research team with the aim of making scientific contributions to empirical and theoretical studies in the linguistic anthropology of education and associated disciplinary areas. WP3: Exploit findings from WP1 and WP2 to design, promote, and launch open-access, research-driven and policy- informed teacher education materials for teachers who (will) work in multilingual communities in Europe. After data collection and subsequent engagement with the fieldsites is complete, WP3 involves the creation of teacher training materials for pre-service and in-service teachers working in linguistically diverse settings. This phase draws on pseudonymized and, when possible, anonymized Type A-B-C-D-E data in the generation of Open Access materials and courses, and will be facilitated by short visits and a secondment with collaborating research centers and teacher training programs. Courses and repositories of materials for teachers working with multilingual students and/or in linguistically diverse settings will be made available in an Open Access format that is yet to be determined, but may resemble the mode of presentation used in learninghowtolookandlisten.com. Pseudonymized or anonymized audio, video, and transcript data will be available in Open Access format for researchers in linguistics and education via the TalkBank repository (talkbank.org). Metadata will be generated for all Type C and E data and will be made available on CLARIN.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the expected size of the data that you intend to generate or re-use?
|
I intend to record approximately 10-15 hours of discourse per week (both in the form of classroom discourse and interviews). Over the course of the 2022-2023 academic year, this amounts to approximately 350-500 hours of recordings. Type A & B data will be reviewed and matched up with one another so that classroom events and interviews can be coded qualitatively, compared with Type D data, and then transcribed to produce Type C data. It is unlikely that all recorded discourse will be transcribed over the course of the project. Instead, the researcher will draw on personal observations of the day's events, compare to fieldnotes, and use the fieldnotes as a guide to identify moments of talk that fall within the scope of the project. These moments will be transcribed and will then be coded further so as to identify themes to pursue in analysis. Type E data will be collected via 2-3 short surveys of maximum 5 questions each from approximately 90-100 participants.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Data Summary
|
What is the origin/provenance of the data, either generated or re-used?
|
The discourse data will be obtained via recordings of classes in session and interviews with teachers at two middle schools. Transcripts and codes will be produced by the researcher and other team members. Surveys will be gathered from student and teacher participants.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Data Summary
|
To whom might your data be useful ('data utility'), outside your project?
|
The data will be useful to two groups of users: 1. Applied linguists (for discourse analysis, interactional sociolinguistics, second language development, etc.), linguistic anthropologists (enregisterment, speech chains, etc.), anthropologists of education (academic discourse socialization, student identity development, etc.) 2. Teachers and teacher trainers (in pre-service teacher education courses at universities, in professional development initiatives at schools and school boards for in-service teachers, for practitioner-researchers in their own classrooms)
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will data be identified by a persistent identifier?
|
Each transcript and media file in TalkBank is assigned a Permanent ID via the Handle System (www.handle.net), and each corpus has an ISBN and DOI (digital object identifier) number. Data deposited in CLARIN will be assigned handles.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will rich metadata be provided to allow discovery?What metadata will be created? What disciplinary or general standards will be followed? In case metadata standardsdo not exist in your discipline, please outline what type of metadata will be created and how.
|
Rich metadata will be provided to allow for discovery of Type A-B-C-E data. The DDI standards will be followed in order to maximize data discoverability (https://ddialliance.org/). LRMI schemas will be followed in the labeling of data archived in TalkBank (https://www.dublincore.org/specifications/lrmi/concept_schemes/). TEI standards for ensuring findability of texts deriving from transcribed speech will be followed wherever possible for Type C data (https://tei-c.org/release/doc/tei-p5-doc/en/html/TS.html). The vocabulary used for creating metadata descriptions will follow CESSDA standards (https://www.cessda.eu/Tools/Vocabulary-Service).
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will search keywords be provided in the metadata tooptimize the possibility for discovery and then potential re-use?
|
The indexing and registration of materials in TalkBank have been adapted to align with OLAC (Online Language Archives Community) at www.language-archives.org and VLO (Virtual Language Observatory) at https://vlo.clarin.eu . As is stated on the TalkBank website, "These systems allow researchers to search for whole corpora or single files, using terms such as Cantonese, video, gesture, or aphasia. In order to publish or register TalkBank data within these systems, we create a 0metadata.cdc file at the top level of each corpus in TalkBank. Some of the fields in this metadata file are designed for indexing in OLAC and some are designed for the CMDI system used by VLO and the related facility called The Language Archive (tla.mpi.nl). Because of the highly specific nature of the terms and the software used for regular harvesting and publication of these data, we do not require users to create the 0metadata.cdc files. The following table explains what keywords are expected within each field of these files. The first fields listed are for OLAC and the later ones are for CMDI. For CMDI, the values unknown and unspecified are also available for most of the fields. ... We use a CLAN program that takes the information from the 0metadata.cdc files and from the header lines in each transcript." For more information about the specific fields, please see the table in Section 6.2: https://talkbank.org/manuals/CHAT.html#_Toc107417263
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data findable, including provisions for metadata: Will metadata be offered in such a way that it can beharvested and indexed?
|
Please see above.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Will the data be deposited in a trusted repository?
|
Yes, data will be deposited in TalkBank's repositories ClassBank and/or BilingBank, as well as in the Eurac Research node of CLARIN.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Have you explored appropriate arrangements with the identified repositorywhere your data will be deposited?
|
For TalkBank, STEMCo audiovisual and transcript data has been provisionally accepted. The final decision will be made on the basis of the filters used for pseudonymization of audiovisual materials, since TalkBank does not typically accept "doctored" audiovisual materials. In the event that I am not able to deposit edited video recordings in TalkBank, I will either (a) submit only transcripts and accompanying audio data or (b) submit transcripts, accompanying audio data, and a visual representation (a diagram) of the seating arrangement during the speech event. The Eurac node of CLARIN does not currently host audiovisual files. I will therefore only deposit textual data and survey results (Types C, D, E).
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
Making data accessible - Repository: Does the repository ensure that the data is assigned an identifier? Will therepository resolve the identifier to a digital object?
|
Yes. See above.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
FAIR data
|
why and how long this will apply, bearing in mind that research data should be made available as soon as possible.
|
There is no embargo.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Other research outputs
|
In addition to the management of data, beneficiaries should also consider and plan for the management of otherresearch outputs that may be generated or re-used throughout their projects. Such outputs can be either digital (e.g.software, workflows, protocols, models, etc.) or physical (e.g. new materials, antibodies, reagents, samples, etc.).
|
Other research outputs in addition to data will include teacher training materials in the form of an open access teacher training course (or a series of independent modules), as well as digital materials that can be used online or printed out and used in class.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Other research outputs
|
Beneficiaries should consider which of the questions pertaining to FAIR data above, can apply to the management ofother research outputs, and should strive to provide sufficient detail on how their research outputs will be managedand shared, or made available for re-use, in line with the FAIR principles.
|
Open access publications will be indexed with appropriate metadata based on the publisher's conventions, with persistent identifiers, and will be findable via Google Scholar, university library searches, and other conventional means of accessing research materials. All open access publications will be deposited in trusted repositories such as Zenodo. Teacher materials will be made available at an accessible URL (no password, no email address, no login information required). In order to avoid any type of membership, registration, or subscription, the costs of hosting these textual and audiovisual materials for at least ten years will be paid up front in order to guarantee their availability even without a DOI. Once the materials have been created and are in the piloting phase, the PI will seek out a permanent "home" for them (e.g., European Centre for Modern Languages, a university, Eurac Research, or the Teacher Academy ). This, however, cannot be determined until the final product of STEMCo has been drafted and piloted.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
What will the costs be for making data or other research outputs FAIR in your project (e.g. direct and indirect costsrelated to storage, archiving, re-use, security, etc.) ?
|
There is no direct cost for hosting data on TalkBank or the Eurac node of CLARIN. Open Access publications that come out of STEMCo will likely cost approximately €2500-3000 each, and there may be as many as five or six of these. Some will be covered by the grant awarded by the European Commission and others of these costs will be covered by Eurac Research. I anticipate hiring an external collaborator to assist with metadata creation and other technical aspects of data management for approximately €8000-10.000 total. Hosting the teacher materials in an entirely open format for at least 10 years will also likely cost approximately €1500-3000. I anticipate spending approximately 3-6 person months on various aspects of the FAIRification of data for my project.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
How will these be covered? Note that costs related to research data/output management are eligible as part of theHorizon Europe grant (if compliant with the Grant Agreement conditions)
|
These costs will be covered either by the MSCA grant itself, by the host institute (Eurac Rsearch), or by other funds made available by third parties.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
Who will be responsible for data management in your project?
|
The project leader/PI, Andrea Leone-Pizzighella, is responsible for data management during the project. TalkBank and CLARIN are responsible for long-term preservation once the data from STEMCo have been accepted.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Allocation of resources
|
How will long term preservation be ensured? Discuss the necessary resources to accomplish this (costs and potentialvalue, who decides and how, what data will be kept and for how long)?
|
Long-term data and metadata preservation for TalkBank is guaranteed by Carnegie Mellon University's KiltHub, as described here: https://www.talkbank.org/info/CMU_Support.pdf The Eurac node of CLARIN is linked to the broader CLARIN network which ensures long-term preservation of data and metadata should the Eurac node close. After the 36 months of the project have ended, the original data will have already been destroyed, and only the pseudonymized and anonymized data will be retained (in the above two repositories and the teacher training website).
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Data security
|
What provisions are or will be in place for data security (including data recovery as well as secure storage/archivingand transfer of sensitive data)?
|
All data will also be stored in Sharepoint at Eurac Research which relies on Microsoft Authenticator for access. The folders on the PI's desktop and on the server will be encrypted, and the passwords for encryption management will be stored securely by the PI and by the ICT department of Eurac. All files on the PI's desktop are automatically backed up to Sharepoint as soon as the computer is connected to the internet and the VPN. Eurac is ISO certified -- Quality Management System (9001) and Information Security Management (27001)
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
Ethics
|
Are there, or could there be, any ethics or legal issues that can have an impact on data sharing? These can also bediscussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapterin the Description of the Action (DoA).
|
The raw discourse data (audio, video) cannot be shared because the ethnographic nature of the project may reveal sensitive personal data about participants. The survey data that is made publicly available will only be published in aggregated form. Selections of audio and video, pending the functionality of audiovisual filters, may be made available in a public repository. Transcripts of discourse data will be pseudonymized and made publicly available in excerpted form so as to ensure that no sensitive data is shared.
|
58bac0dca4a95cbb7a7806c3104b00cf
|
dmponline.dcc.ac.uk
|
1. General features
|
Acronym/short study title
|
Criterion and construct validity of the RISE device to assess movement behavior in people with stroke
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
1. General features
|
Path of the Research Folder
|
\\ds\GROUPS\HER\Onderzoek\Revalidatie\StudentenFW\Lauravanderheiden
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
1. General features
|
METC number (only for human-related research)
|
15-768/C
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
1. General features
|
Name of datamanager consulted
|
Dorien Huijser, Research Data Management Support, Utrecht University
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
2. Data Collection
|
Give a short description of the research data.
|
Subjects Volume Data Source Data Capture Tool File Type Format Human 20 self-designed questionnaire demographic data Quantitative .csv/.xls Human 20 Barthel Index assessment by trained healthcare professional Quantitative .csv/.xls Human 20 Functional Ambulation Category assessment by trained healthcare professional Quantitative .csv/.xls Human 20 10-meter walking test assessment by trained healthcare professional Quantitative ..csv/.xls Human 20 RISE device RISE device activity monitor Quantitative .csv/.xls Human 20 ActivPAL ActivPAL activity monitor Quantitative .csv/.xls Human 20 MoveMonitor Dynaport MoveMonitor Quantitative .csv/.xls Human 6 Video recording JVC Camcorder GZ-R495BE Video footage MP4 Volume: the table above depicts the data yet to be gathered.
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
2. Data Collection
|
Describe the flow of the data (name systems used and/or third parties, recipients) <add link to location wherediagram is stored in RFS>
|
The flow of data in the study involves multiple devices and software applications: Newly collected data: RISE device 1. Pseudonymization of personal information before Measurement 2. Measurement with RISE Device: Collect data using the RISE device. 3. Raw Data on RISE Device: unprocessed data on the RISE device. 4. Categorization into Movement Behaviors: Use 2M Engineering software to categorize data into movement behaviors 5. Secure USB Transfer to Research Folder: Transfer categorized data securely via USB to a research folder. 6. Compilation in Excel Files: specifying start and end times for each activity segment. 7. SPSS Analysis: Analyze compiled data in SPSS for further insights. ActivPAL/MoveMonitor: The above steps outline the flow of data for both the RISE device and ActivPAL raw data, highlighting the specific processes involved in each stage of data handling and analysis. Only other software: ActivPAL: software version 7 PAL technologies MoveMonitor: Dynaport MC.Roberts software (Online) Video 1. Pseudonymization of Personal Information Before Recording 2. Video Recording: video footage on camera 3. Storage on Research Folder Structure: Save the recorded video footage in a designated location within the research folder structure. Ensure that the storage location is secure and follows ethical guidelines. 4. Data Analysis - Categorization by Researchers: Two researchers analyze the pseudonymized video footage to categorize data into different movement behaviors. 5. Compilation in Excel Files: Compile the categorized data from the video analysis into Excel files. These files may include details about the identified movement behaviors. 6. SPSS Analysis: Further analyze the compiled data in statistical software such as SPSS for deeper insights and statistical exploration. Barthel index/FAC score/10 meter walkingtest. 1. Pseudonymization of Personal Information Before Measurement 2. Paper Form: Use of paper forms for administering the Barthel Index, FAC score, and 10-meter walk test. 3. Paper Record of Results: Record the results of the assessments on the paper forms. 4. Transfer to Excel on Secure Drive: Transcribe the recorded results from the paper forms into an Excel spreadsheet. Save the Excel file on a secure drive, ensuring it is part of a protected folder or structure. 5. Secure Storage of Paper Forms: Store the original paper forms securely in a locked cabinet Self-Designed Questionnaire: 1. Paper Form 2. Patient Completion Patients/researcher will fill out the paper form with their responses. 3. Transfer to Excel on Research Folder Structure: Transcribe the paper-based responses into an Excel spreadsheet. Save the Excel file on the secure drive within the research folder structure. 4. Secure Storage of Paper Forms: The physical paper forms are stored securely in a locked cabinet within the UMCU. Reused Data: Accelerometer/Video/Barthel Index/FAC Score/10 Meter Walking Test/Self-Designed Questionnaire Files are stored in the research data folder, protected by permission rights (UMC Utrecht O:drive ->revalidatie->studentenFW- >lauravanderheiden-> reuse raw data collection). Paper forms are secured in a locked cabinet. The analysis process for the reused data follow the same procedures as the data to be collected.
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
2. Data Collection
|
Specify data management costs and how you plan to cover these costs.
|
Type of costs Division ("overhead") Department Funder Other (specify) 1. data management such as storage during collection and analysis. x 2. Time spend creating and upkeep the plan voluntary 3. additional costs: data collection (including written materials and activity monitors needed), data preparation for analyses, additional analyses programs. x 4. data capture tool license fee Mc Roberts MoveMonitor x The data capture tool for the MoveMonitor is the only paid component; software for the RISE device and ActivPAL is free
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
2. Data Collection
|
Which contracts are in place?
|
Organization Contract Type with UMCU MoveMonitor MC Roberts Data Processing Agreement Fontys Hogeschool Consortium agreement
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
2. Data Collection
|
State how ownership of the data and intellectual property rights (IPR) to the data will be managed
|
Our dataset comprises activity monitor data, video footage, assessments, and questionnaires from individuals who have had a stroke, including data collected by Fontys Hogeschool under a consortium agreement. Fontys Hogeschool has contributed to the data collection but will not assume ownership of the collected data for this study. UMC Utrecht remains the owner of all data collected for this study. IPR is not applicable to this data. In the event of potential research collaboration projects, pseudonymized data may be shared after the formulation of a Data Transfer Agreement. Although IPR protection cannot be applied to our data, its intrinsic value will be considered when making the data available to others, when setting up research collaborations, and when drafting Data Transfer Agreements
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
3. Data Protection Impact Assessment (DPIA)
|
What type of directly or indirectly identifying personal data will be used? Indicate why you need this data. Is thistruly necessary?
|
Type of personal data Reason for collecting these data Name to communicate with participants and give information, ensuring a personalized and responsive interaction throughout the research process while maintaining strict confidentiality and privacy standards. Address to do home visit for participants only involving construct validity Telephone number to get in contact with the participant, answer questions from the participant Age (if fine grained) to analyze and interpret the study results with consideration of potential age-based variations. Gender to analyze and interpret the study results with consideration of potential gender-based variations. Imaging e.g. MRI, pictures or video (can be health data) The necessity of collecting this data is typically linked to the research objectives, and in this case, it's for assessing the criterion validity of the intervention or methodology. Criterion validity often involves comparing the results of a measurement or diagnostic tool with a gold standard or reference, and in your case, video data serves as a reference point. Length weight type of stroke infaction yes/no hemisphere right or left time since stroke These measurements are related to the anthropometric characteristics of the participants and can be grouped for simplicity. They contribute to adjusting accelerometer data for individual body size. By collecting data on stroke characteristics, including type of stroke, infarction status, and affected hemisphere, we aim to enhance the generalizability of our study results to a more specific population. Understanding the nuances of stroke subtypes and affected hemispheres allows for a more nuanced interpretation of our findings, increasing the relevance of our results to individuals with similar stroke profiles.
|
ab4d4d3d1af08a1abc20b7ecb94a076d
|
dmponline.dcc.ac.uk
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.