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Data Sharing
|
How will you share the data?
|
Data would be shared online using Google forms. Participants would be recruited through emails, social media, networking sites, and through snowballing techniques.
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ff8f0398804c258b47bffeeb3d766018
|
dmponline.dcc.ac.uk
|
Data Sharing
|
Are any restrictions on data sharing required?
|
Yes, restrictions to data sharing would be implemented due to the researcher's need to maintain the confidentiality and anonymity of participants.
|
ff8f0398804c258b47bffeeb3d766018
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
The researcher alone would be responsible for the data management plan (DMP), ensuring that it is reviewed and revised as appropriate.
|
ff8f0398804c258b47bffeeb3d766018
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
Knowledge of software such as Google forms, SPSS, and so on would be required. Basic skill in data interpretation and analysis would also be required
|
ff8f0398804c258b47bffeeb3d766018
|
dmponline.dcc.ac.uk
|
1. What data will be collected or produced, and what existing data will be re-used?
|
If new data will be produced: describe the data you expect your research will generate and the format andvolumes to be collected or produced.
|
Data Category Data source/Description Format Size or amount Pre-production & Production Digital- monitoring data Geophones- seismic waveform data mSEED 6.7 GB/day; 0.6 TB/year (collected for 90 days) Pre-production & Production Digital- monitoring data EM surface network- 10 receivers + source To be determined 500 GB/month; 2 TB/year (collected for 4 months) Implementation Digital- monitoring data Logging data LAS MBs-GBs Implementation Digital- monitoring data Well testing data Tabular (.xls, .csv, .txt or similar) MBs Implementation Digital- monitoring data Real time operations data 'RTOC' Tabular (.xls, .csv, .txt or similar) GBs Production Digital- monitoring data Distributed Acoustic Sensing (DAS) SEG-Y 400-500 GB/day for main well, 90 GB/day for monitoring well; 40 TB/year total (collected for 90 days in main well, 21 days in monitoring well) Production Digital- monitoring data Distributed Temperature Sensing (DTS) LAS MBs-GBs Production Digital- monitoring data Distributed Pressure Sensing (DPS) LAS MBs-GBs Production Digital- monitoring data Distributed Strain Sensing (DSS) To be determined MBs-GBs Production Digital- monitoring data Hydrogeochemistry bypass Tabular (.xls, .csv, .txt or similar) MBs-GBs Production Digital- monitoring data Well production data Tabular (.xls, .csv, .txt or similar) MBs-GBs Pre-production Physical sample(s) Groundwater samples Physical Samples To be determined Implementation Physical sample(s) Cuttings Physical Samples To be determined Implementation Physical sample(s) Rock cores Physical Samples 300 m of cores maximum Implementation Physical sample(s) Down hole fluid sampling Physical Sample(s) 1 to several samples total Implementation Physical sample(s) Sand Physical Samples To be determined Production Physical sample(s) Water samples from sample taps Physical Samples Samples taken at most once per day; 100s of samples per year (collection schedule not yet known) Pre-production Digital- derived from sample Groundwater analyses Tabular (.xls, .csv, .txt or similar) KB/sample Implementation Digital- derived from sample Mud logs Tabular (.xls, .csv, .txt or similar) MBs-GBs Implementation Digital- derived from sample Core logs Text (PDF) MBs-GBs Implementation Digital- derived from sample Core photos Image (JPEG) 500 GBs Implementation Digital- derived from sample Core scans .IMA, .TIFF 2 TBs Implementation Digital- derived from sample X-ray diffraction analyses of samples Tabular (.xls, .csv, .txt or similar) MBs Implementation Digital- derived from sample X-ray fluorescence analyses of samples Tabular (.xls, .csv, .txt or similar) MBs Production Digital- derived from sample Cation analyses of water samples Tabular (.xls, .csv, .txt or similar) KB/batch of analyses; KBs-MBs per year Production Digital- derived from sample Anion analyses of water samples Tabular (.xls, .csv, .txt or similar) KB/batch of analyses; KBs-MBs per year Production Digital- derived from sample Gas analyses of water samples Tabular (.xls, .csv, .txt or similar) KB/batch of analyses; KBs-MBs per year Production Digital- derived from sample Manual measurements on water samples Tabular (.xls, .csv, .txt or similar) KB/sample; KBs-MBs per year
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
1. What data will be collected or produced, and what existing data will be re-used?
|
How much data storage will your project require in total?
|
The storage required is estimated to be approximately 43-45 TB per year. Physical samples (cores, sand, cuttings and fluids) will be stored at TU Delft for at least 5 years. Half of each core will be transferred to the national core repository of the Dutch Geological Survey (TNO) in Zeist for archiving as required by the Dutch Mining Act.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
2. What metadata and documentation will accompany the data?
|
Indicate what documentation will accompany the data.
|
All datasets published at 4TU.ResearchData or the SURF Data Repository will be accompanied by README files providing documentation necessary for data re-use data. Guidance provided by 4TU.ResearchData will be followed when preparing the README files. The files will include definitions of all variables used in the datasets, as well as units of measurement. Additional metadata may also be included in the documentation, and is described in section 2.2.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
2. What metadata and documentation will accompany the data?
|
Indicate which metadata will be provided to help others identify and discover the data.
|
4TU.ResearchData All scientific data except for seismic waveform data and potentially DAS data will be made openly available through 4TU.ResearchData . 4TU.ResearchData is a trusted and certified research data repository (it has a Data Seal of Approval certification). All datasets will be accompanied by rich metadata (adhering to the DataCite metadata standard) to ensure that they are findable. In addition, to further aid their discoverability, keywords describing the datasets will be added. 4TU.ResearchData also uses schema.org metadata, meaning that all datasets are indexed in Google Dataset Search. Every dataset will be also assigned a Digital Object Identifier (DOI), to make them citable and persistently available. Discipline-specific metadata developed within the EPOS project may also be included with the data, preferably in custom metadata fields defined on the 4TU.ResearchData repository. Use of this metadata is dependent on future community efforts within the EPOS project to define metadata schemas for ‘Geo-Energy Test Beds’, the EPOS community related to DAPWELL and other living laboratories. If the required metadata schemas are developed within the EPOS project, they will make it possible for EPOS-maintained data catalogs and portals to harvest the metadata for DAPWELL datasets, leading to improved discoverability of the data within the geologic community. If possible, International Geo Sample Numbers (IGSNs) will be acquired for all samples collected as part of the project. IGSNs are persistent identifiers for physical samples. They will be included in the documentation of all datasets relating to physical samples that are published on 4TU.ResearchData. The System for Earth Sample Registration (SESAR) has been contacted about acting as an allocating agent for DAPWELL IGSNs, but this is not yet confirmed because SESAR is developing a new business model that is not yet ready to be shared. If IGSNs are allocated by SESAR, metadata relating to each registered sample will also be made publicly available in the SESAR sample catalog . Each sample receives its own page in the online catalog where the associated metadata is displayed (see an example here ). These pages can also be linked in publications, making sample metadata easily findable via relevant journal articles. The metadata schema used for sample registration is available on GitHub. For more information about the benefits of using IGSNs, see Lehnert et al. (2020) . In the case that it is not possible to acquire IGSNs, unique identifiers will still be assigned to samples within the project and included in the documentation and datasets. Metadata developed internally within the geothermal well project will be included in the documentation accompanying datasets where appropriate. This includes metadata related to lithology, instrumentation, and measurement types/locations/depths/dates/times. ORFEUS data center (seismic waveform data) Seismic waveform data shared via the ORFEUS data center will be accompanied by Station XML metadata ( http://www.fdsn.org/xml/station/ ), following the schema developed by the International Federation of Digital Seismograph Networks. SURF Data Repository (DAS data, if required) If it is not possible to share DAS data via the 4TU.ResearchData repository, it will be published in the SURF Data Repository accompanied by DataCite metadata and a DOI.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
3. How will data and metadata be stored and backed up during the research?
|
Describe where the data and metadata will be stored and backed up during the project.
|
Monitoring data will initially be transferred to a ‘control room’ located near the well. Interrogators to receive and process fibre optic data will also be located there, along with local data storage. Monitoring data will be transferred via a dedicated connection from the control room to a data center maintained by TU Delft ICT services, where it will be stored and automatically backed up during research. Other types of data will also be stored in the data center, but will not need to be transferred via the control room.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
4. How will you handle issues regarding the processing of personal information and intellectual property rights and ownership?
|
How will ownership of the data and intellectual property rights to the data be managed?
|
After the partners form the Geothermie Delft company to operate the well, an R&D agreement will be signed that includes agreements on data ownership and IP rights. It is expected that TU Delft will have ownership of all scientific data collected in the geothermal project, whereas the well operator will own any operational data collected. During the active phase of research, the data manager/engineer, in consultation with the scientific head and programme manager, will oversee the access to scientific data (and other outputs), as well as any requests for access from external parties. Related projects that require use of the scientific data, for example EASYGO , will be given full access to the relevant data. Data will be released publicly no later than at the time of publication of corresponding research papers.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
5. How and when will data be shared and preserved for the long term?
|
How will data be selected for long-term preservation?
|
The project has the potential to generate large volumes of data, requiring careful consideration of what data are selected for long- term preservation. Some data types will be fully preserved (in particular, data collected during the implementation phase, and data derived from samples). The decision about what monitoring data to select for long-term preservation will be undertaken by the scientific team after initial data collection begins. Data underlying scientific publications will always be preserved.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
5. How and when will data be shared and preserved for the long term?
|
In which repository will the data be archived and made available for re-use, and under which license?
|
All data except for seismic waveform data and potentially DAS data will be published at 4TU.ResearchData, which is a trusted and certified research data repository (Data Seal of Approval certification). All datasets will be licensed under a CC-BY license which requires attribution/credit for the original creation, while at the same time ensures broadest possible re-use. All datasets will be accompanied by rich and descriptive metadata, compliant with the DataCite metadata schema, to ensure that all datasets are findable and accessible online. See https://data.4tu.nl/info/en/ for more information. Seismic waveform data from geophones will potentially be shared via KNMI’s ORFEUS data center ( https://www.orfeus- eu.org/data/odc/) , which provides access to data through the web interfaces and web services of the European Integrated Data Archive (EIDA; http://orfeus-eu.org/data/eida/). The waveform data will be accompanied by Station XML metadata ( http://www.fdsn.org/xml/station/ ), following the schema developed by the International Federation of Digital Seismograph Networks. As described on the KNMI Seismic & Acoustic Data Tools page ( http://rdsa.knmi.nl/ ), a CC-BY license is applied to data unless otherwise specified. If it is not possible to share DAS data via the 4TU.ResearchData repository, it will be published in the SURF Data Repository , which specializes in storing large volumes of data in the TB-PB size range. Any data stored in this repository will be accompanied by DataCite metadata and a DOI, and published under a CC-BY license.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
5. How and when will data be shared and preserved for the long term?
|
Describe your strategy for publishing the analysis software that will be generated in this project.
|
There are currently no plans to generate software as part of this project.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
6. Data management costs
|
What resources (for example financial and time) will be dedicated to data management and ensuring that data willbe FAIR (Findable, Accessible, Interoperable, Re-usable)?
|
4TU.ResearchData is able to archive 1TB of data per researcher per year free of charge for all TU Delft researchers. This free storage is sufficient for publishing most data types in the project in a FAIR manner. 4TU.ResearchData also provided a letter of support for the EPOS-NL proposal indicating their willingness to facilitate the publication of data from the project. Additional funds have been included in the EPOS-NL project budget for data-related costs, which can be used for costs related to the publication of DAS data. In terms of personnel, the dedicated data manager/engineer hired in the project will be responsible for data management in the project.
|
f94eead3f5ed6bcbf4206356cc97c5ab
|
dmponline.dcc.ac.uk
|
Data Collection
|
What data will you collect or create?
|
Dataset: 1. ICO trust signals 2. ICO funding targets 3. the amount of money raised by every ICO 4. related ICO information ICO trust signals are counted from the whitepapers of ICOs. It is the measurement we measure trust signals of ICOs. ICO funding targets are the goals ICO wants to achieve in the financing activities, which are set by the founders and the teams of ICOs. The amount of money raised is the funds raised in the duration of ICOs. Related information such as ICO duration, ICO whitelist, KYC, MVP, the country of ICOs locate etc. are the control variables which will be used in the regressions. Formats Excel for all raw data. Limited volume, in the order of Mb.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Data Collection
|
How will the data be collected or created?
|
Firstly, collect the whitepapers of ICOs. Whitepapers are from every ICOs' own websites. Use python to split the words in whitepapers and count the number of trust signals. Other ICO information, including ICO funding targets, the amount of money raised, ICO duration, ICO whitelist, ICO team size etc. will be collected from every ICO websites and integrated websites. The followers of ICO Facebook and Twitter will also be collected from their profiles.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
The papers wriiten during the PhD time will include the methodology to explain how we collect and process data. The methods used, the definitions of variables, and research hypothesis will all included. The documentation will follow the community metadata standards (Directory of Metadata Standards) where necessary.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
All public domain data - no particular ethical issues
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage copyright and Intellectual Property Rights (IPR) issues?
|
In accordance with the relevant rules, to ensure both own rights and intellectual property rights within protection. No copyright issues from public domain info.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will the data be stored and backed up during the research?
|
The data will be stored in personal laptop and be backed up in the cloud software.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will you manage access and security?
|
Personal password secure. No other access.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
Which data are of long-term value and should be retained, shared, and/or preserved?
|
All data will be saved in case that there is need in further research.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
No plans to destroy data - all data will be preserved in personal laptop and be backed up.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Data Sharing
|
How will you share the data?
|
Though CORD (https://cord.cranfield.ac.uk) Cranfield University's research data repository, as necessary.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Data Sharing
|
Are any restrictions on data sharing required?
|
Raw data in public domain so few restrictions on sharing expected. If any proprietary data is developed that will be confidential to the PhD.
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
Haofeng Xing
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
All data in public domain so no additional resources are required
|
bd4a4b5efbf1ad84cef21529836c3892
|
dmponline.dcc.ac.uk
|
Data Collection
|
How will the data be collected or created?
|
Data will be collected using the Good With web app running on lab computers. Participants will enter data and respond to questions in the web app using the mouse and keyboard, and the data will be written to a Google Cloud Firestore database. Firestore databases are structured into nested collections and documents. User personal data will be stored in the document 'users/{user_identifier}/'. Psychometric answers will be stored in the document 'users/{user_identifier}/psychometric- answers/{psychometric_test}'. Banking data will be stored in nested documents and collections inside 'banking/{user_identifier}/'. Field names inside each Firestore document are named in camelCase, with collection names in dash-case and document names in snake_case. Processed data will be stored in Google BigQuery. These data will be stored in SQL tables that are themselves nested within datasets. For instance, raw and processed psychometric data will be stored in different tables inside a ‘psychometrics’ dataset. However, banking data will be stored in tables inside a different ‘bank_transactions’ dataset. All dataset, table, and column names will be named in snake_case. For quality control, we will implement various rules to ensure participants complete the psychometrics in a valid manner. For instance, we will require participants to respond to each questionnaire item, and will implement an attention check to test whether participants are carefully considering written instructions. We will also require participants to reconsider their responses if we detect they are responding according to a pattern that signifies a lack of engagement with the instructions (for instance, responding in a straight line down the matrix of questionnaire items). We will also require users to authenticate via their bank accounts when giving their Open Banking data, to ensure the data being passed through is their own. Users will be directed to and from bank authentication screens via our web application.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
Due to the sensitive nature of the data being collected and its commercial value to the funder, the data from this study will not be accessible to secondary users. The research team will maintain internal documentation that contains contextual information about the data, how it was collected, definitions of key variables, assumptions, units of measurement and analytical information. All analysis code will include inline comments that detail data cleaning and analysis steps in plain language.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
Participants will give fully informed consent to sharing their data, and for their data to be preserved. They will do this by reading a brief and consenting prior to the start of the study, and by reading and accepting consent agreements in the Good With web app. Participants will also be able to withdraw from the study at any time without penalty and will be provided with the FoH ethics administrator’s details in the event that they have ethical concerns about the study. As previously stated, we must keep a record in Cloud Firestore of participant names against their unique identifiers. However, in all other storage locations, all participants’ data will be associated with a randomly assigned user reference code only. This code will identify the participant in all subsequent data analyses. All databases will require secure authentication, and sensitive data (e.g. participant identifying information, demographics, and banking data) will never be copied outside of these databases.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage copyright and Intellectual Property Rights (IPR) issues?
|
Good With Limited are the sole owners of all data collected, created and otherwise processed through the course of this project. No data will be shared with third parties during the course of this project, however models derived from data collected in this research may form part of the commercial offering - Good With inside (GWi), which is owned in full by Good With Limited, and will be licenced to customers in the financial services industry for the purpose of determining credit applicants’ creditworthiness. Copyright and IPR of all data collected, created or otherwise processed is retained by Good With Limited. Where a scientific publication is considered, data will be aggregated and all personally identifying information redacted to ensure full compliance with UK-GDPR. We will not provide open source data with any publications, however we will share, at our own discretion, samples of data for the purpose of scientific research.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will the data be stored and backed up during the research?
|
All raw data from the Good With web app (participant responses by mouse or text input) will be stored in a Google Cloud Firestore database, in NoSQL format. Processed data that has undergone statistical analysis or transformation will be stored on the same Google Cloud Firestore database and in a Google BigQuery database. All cloud data will be stored in Google servers located in London, UK; the same legal jurisdiction that the data will be collected in. All data from the Good With web app used in this study will be automatically stored in the cloud and periodically backed up using an automatic process. This avoids problems associated with storing data on physical storage drives. Data from qualitative user interviews will be stored in audio recordings with common file formats (.wav, .mp3). Processed data from the thematic analysis of these recordings will be stored in .docx files. These files will be stored in password-protected Google Drive folders that are only accessible to the research team. Consent form results will also be stored on Google Drive with the same security measures in place. All databases used in this study will have disaster-recovery procedures in place in the rare case of cloud service provider failures or human error when maintaining data. This will allow the retrieval of previous versions of files or databases, minimising the potential for data loss. The PI will be responsible for ensuring that these systems are in place, and that collected data is being stored following the data management plan.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will you manage access and security?
|
Both Google Cloud Firestore and Google BigQuery are password-protected cloud databases that can only be accessed by members of the Good With data and development team. Both databases have Identity and Access Management security measures, meaning that permissions to view, query and write to these databases must be explicitly granted by an administrator. Both databases are encrypted by default. To query or write data to BigQuery, each member of the research team must authenticate using a unique service account key that is either stored in a password-protected location on their local machine or as a secret hosted in a password- protected area in Google Cloud Platform. Data stored on Google Drive cloud storage will also require password authentication to access. Access permissions will also be restricted to members of the research team, meaning that unauthorised user accounts cannot access these data even with a valid password. The transfer of data from the Good With app to cloud storage is secure and automated, using the service account mechanism detailed above. Any transfer of data from password-protected local devices to Google Drive cloud storage will take place immediately after each data collection session. Locally-stored copies of participant data will be deleted once this transfer has taken place, to reduce the risk that these data are compromised in any way.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
Which data are of long-term value and should be retained, shared, and/or preserved?
|
All data will be retained to provide participants with a continued service should they wish to use the Good With web or mobile apps after the study ends. These data may also be used in further research on financial behaviour and user experiences of the app. Data from qualitative interviews will also be preserved for potential use in further research and product development. Data will be held for a minimum of 6 years following Good With Ltd’s privacy policy, after which it will be fully anonymised and stored indefinitely for model integrity and development. This is in line with the University of Plymouth Research Data Policy.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
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Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
All stored data above will be preserved for a minimum of 6 years, and then fully anonymised and held indefinitely, following the University of Plymouth Research Data Policy. Data will be held in the BigQuery and Firestore databases managed by Good With Ltd for this duration of time since the data pertains to app user accounts. Any preparation and storage costs incurred will be covered by Good With Ltd as part of their existing data storage plans.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
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Data Sharing
|
How will you share the data?
|
The data to be collected contains some sensitive information. Namely, participant financial information, and demographic data. Records will not be fully anonymised when stored in Cloud Firestore, as records of participant names and unique identifiers must be kept to process account deletion requests and for continuity if the participant uses other Good With services. Data stored in Google BigQuery is pseudo-anonymised with a unique identifier being associated with each participant record. As such, participants may be at least partially identifiable from their data if it were shared. The data stored as part of this study also closely relate to the Good With Readiness Score in that key measures derived from the data determine the final score. Sharing the data from this study could allow secondary users to reverse engineer the scoring methodology, violating intellectual property rights. To mitigate these anonymity and IP concerns, we have decided not to share data with any secondary users. Our results will be disseminated in scientific publications, whitepapers, internal reports, and conference presentations, but the raw data will not be made publicly available. Good With Ltd may choose to share samples of their fully anonymised data for scientific research purposes at a later date, at their discretion.
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faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
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Data Sharing
|
Are any restrictions on data sharing required?
|
Due to intellectual property rights, Good With Ltd has ownership of any data collected through their web app. Since the data for this study will be stored exclusively on Good With Ltd databases, they maintain the right to exclusive access to these data. Details of participant consent will, however, be shared with the University of Plymouth for participant administration purposes. Accordingly, we have included a data-sharing agreement as a supplementary document in our Faculty of Health ethics application.
|
faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
The PI will be responsible for implementing the DMP and ensuring it is reviewed and revised in case of any changes. The PI will engage in data capture, metadata production, data quality, storage, backup, data archiving, and data sharing. The PI will share these responsibilities with the Good With Ltd data and development team.
|
faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
No additional staff or training will be required to deliver this data management plan. Storage, software, and hardware needs will be met by the Good With Ltd, the funding organisation. Storage charges will be paid for by the funding organisation.
|
faea7b6c3ed95b699eb3bd9f177b5789
|
dmponline.dcc.ac.uk
|
Data Collection
|
How will the data be collected or created?
|
Collection of staff data Using the seven constructs of the Theoretical Framework of Acceptability (TFA): affective attitude, burden, ethicality, intervention coherence, opportunity costs or gains, perceived effectiveness, and self-efficacy effectiveness . Proposed secondary outcomes for the full trial (all available through patients’ electronic records) will be: - Patient’s NEWS2 (T0, T1, T2, T3) - Time between trigger event and ward doctor review (T1, T2, T3) - Time between trigger event and CCOT review (T1, T2, T3) - Number of new Do Not Attempt Resuscitation/Treatment Escalation Plan decisions (T1) - Number of planned admissions to a level 2 (high dependency) or level 3 (intensive care) area (T1, T2, T3) - Number of unplanned admissions to a level 2 or level 3 area (T1, T2, T3) - Acute Physiology and Chronic Health Evaluation II score within 24 hours of admission to a level 3 area (T1, T2, T3) - Death (T3) - Time between trigger event and discharge from a level 2 or level 3 area (T1, T2, T3, T4) - Time between trigger event and hospital discharge (T1, T2, T3, T4). Collection of researcher’s reflections from supporting implementation Researcher’s reflections from supporting implementation activities in a coaching role (section 3.5) will be captured through voice and/or journal notes. The person who supports implementation within a cluster (me or a research fellow) will not participate in data collection in the same cluster to minimise confusion from participants and researcher bias.
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3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
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Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
I will be collaborating with the Queen Mary University of London (QMUL) Pragmatic Clinical Trials Unit. They will be building a database for me to record the data. They will also support quality assurance as part of their package of support.
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3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
|
Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
In ACAF year 1, I will seek NHS research ethics, Confidentiality Advisory Group (CAG), and Health Research Authority approvals. Cluster-level recruitment will be formalised, and subsequently eligible wards will be recruited from within each hospital as follows: managers from eligible wards will be emailed to provide brief information about the study, and invited to contact me if they are interested. Following an expression of interest, I will attend the ward with a representative from the hospital CCOT to meet with the ward manager and provide further information. Ward managers will be invited to email me and opt-in if they would like staff from their ward to participate. Any inpatient on a participating ward within a cluster who has a trigger event (defined as a first NEWS2 7) at any time point during the feasibility trial will be included in the study. It is plausible that any patient on an acute ward could deteriorate at any time, therefore screening and identifying individual patients who will later develop elevated NEWS2 (and become eligible for involvement) would be difficult. Equally, pre-emptively consenting all patients on the ward in case they deteriorate (when only a proportion will) would be impractical, onerous, and could increase patient anxiety. For these reasons, approval will be sought from the CAG to access routinely collected data for a sample of patients on participating wards with a first NEWS2 7 without consent. These decisions were informed by discussions with patient advisors. Throughout data collection, posters will be displayed on participating wards notifying patients and visitors that research is taking place and describing how they can access further information. Staff who agree to complete a questionnaire and/or participate in an audio-recorded interview will be asked to prospectively sign a consent form. The purpose of the observations will be to explore how components of OPTIMISE-NEWS are implemented. Due to the unpredictable clinical context in which some intervention components will be implemented and the group-level mode of delivery, it may not be practical to consent all staff members individually and prospectively for participation in an observation. Consequently, an opt-out procedure will be used .
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3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
|
Storage and Backup
|
How will the data be stored and backed up during the research?
|
I will be collaborating with the Queen Mary University of London (QMUL) Pragmatic Clinical Trials Unit. They will be building a database for me to record the data. They will also support quality assurance as part of their package of support.
|
3c968c3dcacba46adbf0e386278379b1
|
dmponline.dcc.ac.uk
|
Storage and Backup
|
How will you manage access and security?
|
I will be collaborating with the Queen Mary University of London (QMUL) Pragmatic Clinical Trials Unit. They will be building a database for me to record the data. They will also support quality assurance as part of their package of support.
|
3c968c3dcacba46adbf0e386278379b1
|
dmponline.dcc.ac.uk
|
Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
The data will be retained for 10 years.
|
3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
|
Data Sharing
|
How will you share the data?
|
Data Transfer Agreements will be put in place where appropriate.
|
3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
Dr Duncan Smith Colleagues at the QMUL PCTU
|
3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
Collaboration with the clinical trials unit
|
3c968c3dcacba46adbf0e386278379b1
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dmponline.dcc.ac.uk
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Data Collection
|
What data will you collect or create?
|
During the execution of the project, we will collect important data, including: 1- textual results (document in doc format) regarding the electrospinning parameters of the nanofibers; 2- chemical and microscopic characterizations of the nanofibers and nanogels (these data will be numerical in txt format); 3- numerical data (spreadsheets in xls format) on the percentages of controlled release of therapeutic molecules; 4- concentration range for performing daidzein tests (numerical data, database in xls format); 5- histological and molecular biology results will be images in jpg format. All collected data must have a maximum volume of 10 MB.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Data Collection
|
How will the data be collected or created?
|
To generate the data, we will perform: synthesis of thermoresponsive smart nanohydrogels, fabrication of nanofibers, controlled release tests, in vitro cytotoxicity tests in 2D and 3D models and preclinical evaluation of our injectable hydrogel systems. The methodologies that will be used will be laboratory notebooks and the Origin software. There will be no restriction on the data obtained in this project. It is very necessary to generate data in at least triplicates, to ensure the reproducibility of our data since the research is of an innovative nature and there are few data reported to date.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Documentation and Metadata
|
What documentation and metadata will accompany the data?
|
The data generated in this project will be accompanied by descriptive metadata such as “documents” and “images”. Authors and article titles will be saved in the Mendeley database, organized in folders identified with their names. On my computer, the results folders will be identified with the years/test name/date.
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c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Ethics and Legal Compliance
|
How will you manage any ethical issues?
|
Data will be reported responsibly, whenever it is necessary to present them, no empirical data will be omitted, deleted or modified. All confidential data ethics will be maintained until publication. Regarding patient and donor data, these will be pseudonymized and only the surgeon treating the patients will have access to them and knowledge of the pseudonyms. On the other hand, the ethical and legal aspects of this project are applicable in WPs 2 and 3. In WP2, the use of tissue samples obtained from patients is required. In this sense, the protection of the safety of the study subjects, their well-being and their rights will be guaranteed, as stated in the favorable evaluation of CEICA (PI20/429), in accordance with the Data Protection Law in force in Spain. Patients will sign an informed consent that Dr. García-Álvarez will provide them with prior to surgery. In this document, the PI agrees to provide patients with information about the results obtained from their samples upon request. In fact, the PI and the research team will not know the personal data of the patients (the samples will be collected with a numerical code), except for Dr. García-Álvarez, who is the surgeon of these patients. Patients will be adequately informed about the voluntary nature of their participation in this research, the observational nature and objectives of the research in which they are participating, as well as about the protection of personal data.
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c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Ethics and Legal Compliance
|
How will you manage copyright and Intellectual Property Rights (IPR) issues?
|
The data derived from this proposal will be published in Open Access, which allows maximizing the dissemination of the results obtained, avoiding economic or legal implications for the general public to access them.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Storage and Backup
|
How will the data be stored and backed up during the research?
|
The data generated during the research for this project will be saved in the cloud and on hard drives. Backups will be made weekly (every Friday). In the event of a computer incident on the notebook, there will be no significant loss of data, as they will be stored in two different locations.
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c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Storage and Backup
|
How will you manage access and security?
|
Access to the digital results will be managed through passwords, known only to the principal investigator and the two supervisors.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Selection and Preservation
|
Which data are of long-term value and should be retained, shared, and/or preserved?
|
All data generated is equally important. All data will be stored, preserved and available for consultation even after the end of the project. The data derived from the proposal will be published in Open Access and stored in an open access repository (Zenodo), separated and identified by folders related to the experimental results. Both myself and my supervisors Dr. Arruebo and Dr. Mendoza will keep a copy of the data on their computer and/or external hard drive. Access to the data stored in the repository will be allowed by password only to researchers participating in the project.
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c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
|
Selection and Preservation
|
What is the long-term preservation plan for the dataset?
|
The data derived from the proposal will be published in Open Access and stored in an open access repository (Zenodo), separated and identified by folders related to the experimental results.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Data Sharing
|
How will you share the data?
|
The data will be shared through publications of scientific articles, presentations at national and international conferences, seminars and workshops. In addition, they will be shared in the reliable data repository: Zenodo of IISA, which will be partially available from the first year of the project. The full release of all data will be after the 5 articles scheduled to be written are accepted in high-impact journals.
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c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Data Sharing
|
Are any restrictions on data sharing required?
|
Once the data is shared, it will be available to all researchers with a scientific interest.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
|
Responsibilities and Resources
|
Who will be responsible for data management?
|
The principal investigator will be primarily responsible for management activities, data custody (data capture, data production, data quality, storage, archiving and data exchanges.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Responsibilities and Resources
|
What resources will you require to deliver your plan?
|
In this first version of the Data Management Plan, we will not need any financial resources to deliver the data.
|
c1c3c015b0f144e8141a8e9d46c66cab
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dmponline.dcc.ac.uk
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Data types
|
Specify the types of data the research will generate.
|
All digital objects will be managed with the implementation of the FAIR Principles. 1. Digital objects from other sources
* Collecting as many experiment proposals as possible from Diamond Light Source as well as other Photon and Neutron facilities such as the European Synchrotron Radiation Facility.
* Title
* Authors
* Abstract
* References (if applicable)
* DOI (if applicable)
2. Digital objects created from project
* Creating metadata of all the collected experiment proposals. Metadata will be structured in a machine-readable format and linked to the respective experiment proposals.
* Topics
* Experimental techniques
* Producing Python code in Visual Studio Code to collect the experiment proposals and create the associated metadata.
* Collecting web survey responses from Diamond staff and researchers (number to be determined later).
* Creating audio recordings of interviews with Diamond staff and researchers (number to be determined later).
* Creating posters for academic conferences.
* Writing papers and the final DPhil thesis.
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3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
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Data preservation
|
Specify which data will be preserved and how.
|
Deposit of the thesis in the Oxford University Research Archive (ORA) is a mandatory requirement. The Transfer Report will also be uploaded to the ORA after Transfer of Status has been passed. Upload of other data to the ORA will be contingent on approval from Diamond Light Source. All data will be uploaded to and stored on a public GitHub repository (https://github.com/terencetan-c/Project-Stakeholder-Group) . The static DOIs of any material uploaded to the ORA will also be included in GitHub.
|
3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
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Data preservation
|
Specify the software and metadata implications.
|
The Python script used to collect the experiment proposals and create the associated metadata will be provided so that others can reproduce the data. Comments will also be added so that others can understand the code. A README file will be created in the GitHub repository to describe and contextualise the various data files. Metadata will be added when depositing data on ORA. FAIRsharing ( https://fairsharing.org/) will be consulted with to find the most appropriate community standards for each digital object.
|
3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
|
Data preservation
|
Specify for how long the data will be preserved.
|
Data can be stored indefinitely on GitHub repositories and ORA.
|
3a620c0add2380757373a6092dfe33db
|
dmponline.dcc.ac.uk
|
Data sharing
|
Specify and justify which data will have value to others and should be shared.
|
All data will be shared with permission from the University of Oxford and Diamond Light Source.
|
3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
|
Data sharing
|
Specify and justify the length of any proprietary period.
|
All data excluding the code will be uploaded to the GitHub repository as soon as possible, with the exception of information deemed confidential by the University of Oxford or Diamond Light Source. The code will be cleaned up and uploaded together with the submission of the thesis. The thesis will be required to be deposited in ORA after submission.
|
3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
|
Data sharing
|
Specify how data will be shared
|
The data will be available via the GitHub repository. A permanent descriptive record is created for all data deposited in ORA and a Digital Object Identifier (DOI) can be requested. Data in ORA will be discoverable through Google and other search engines.
|
3a620c0add2380757373a6092dfe33db
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dmponline.dcc.ac.uk
|
Resources
|
Specify and justify any resources required to preserve and share the data.
|
GitHub repositories are free to use and require minimal upkeep from users. ORA is currently free of charge, and curation and online delivery of the data will be assured by ORA staff, ensuring the long-term preservation, back-up and accessibility of the data.
|
3a620c0add2380757373a6092dfe33db
|
dmponline.dcc.ac.uk
|
0. Adminstrative questions
|
Provide the name of the data management support staff consulted during the preparation of this plan and the dateof consultation. Please also mention if you consulted any other support staff.
|
Nicolas Dintzner - 23-07-2024
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15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
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0. Adminstrative questions
|
Is TU Delft the lead institution for this project?
|
For the sake of this DMP, we are working in partnership with a university in the US. We'll exchange sensitive material (audio/video recordings)
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
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II. Storage and backup during the research process
|
Where will the data/code be stored and backed-up during the project lifetime?
|
We'll also use SurfFileSender. The partner in the US will handle data storage according to their policies.
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
III. Data/code documentation
|
What documentation will accompany data/code?
|
Only protocol related information (methodology) will be made available
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
IV. Legal and ethical requirements, code of conducts
|
How will ownership of the data and intellectual property rights to the data be managed?
|
For projects involving commercially-sensitive research or research involving third parties, seek advice of your Faculty Contract Manager when answering this question . The activity is framed by a collaboration agreement between TUD and the University in the US (agreement currently in the work).
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
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IV. Legal and ethical requirements, code of conducts
|
Please list the categories of data subjects and their geographical location:
|
The data subjects are members of road safety public administrations and insurance companies located in Europe and in the US (all adults).
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
IV. Legal and ethical requirements, code of conducts
|
What advice did the Privacy team give regarding data transfer? Record below their advice, the data transfermechanism used, and any agreed security measures.
|
The privacy team advised for a joint-controllership agreement (in the work) Pseudo-anonymization of transcripts has been added to our exchange protocol.
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
IV. Legal and ethical requirements, code of conducts
|
Please describe the informed consent procedure you will follow:
|
The researcher will inform the potential participants about the goals and procedures of the research project. The researcher will also inform them about the personal data that are being processed and for what purpose. This information will be provided to the potential participants via email before the experiment. All participants will be asked for their consent for taking part in the study and for data processing by signing a physical/digital informed consent form before the start of the interview/experiment.
|
15994911fce10d7adc41a868da5c2789
|
dmponline.dcc.ac.uk
|
IV. Legal and ethical requirements, code of conducts
|
Where will you store the physical/digital signed consent forms or other types of proof of consent (such asrecording of verbal consent)?
|
Consent form will be stored in the TUD project drive.
|
15994911fce10d7adc41a868da5c2789
|
dmponline.dcc.ac.uk
|
IV. Legal and ethical requirements, code of conducts
|
Where will you store the DPIA report (document on data processing features and document on risk assessment)?
|
The DPIA report is stored as part of the project administrative data on the project drive.
|
15994911fce10d7adc41a868da5c2789
|
dmponline.dcc.ac.uk
|
V. Data sharing and long term preservation
|
How will you share research data/code, including the one mentioned in question 23?
|
The data will be included as supplementary material of the paper, on the publisher's website (Springsevier)
|
15994911fce10d7adc41a868da5c2789
|
dmponline.dcc.ac.uk
|
VI. Data management responsibilities and resources
|
If you leave TU Delft (or are unavailable), who is going to be responsible for the data/code resulting from thisproject?
|
Bob and Alice, researchers in the Car Psychology Lab, [email protected] , [email protected]
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
VI. Data management responsibilities and resources
|
What resources (for example financial and time) will be dedicated to data management and ensuring that data willbe FAIR (Findable, Accessible, Interoperable, Re-usable)?
|
The infrastructure readily available to all researchers of the institution is sufficient for the project.
|
15994911fce10d7adc41a868da5c2789
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dmponline.dcc.ac.uk
|
1. General Information
|
DMP - Contact Person
|
Rosa Hellman, [email protected], Department of People and Society, SLU, https://orcid.org/0009-0008-3923-3713
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
1. General Information
|
Project - Project Title
|
Food waste mitigating activities: the connection of retail and consumers, a scoping review
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
1. General Information
|
Project - Project ID
|
ID for this DMP at SLU: SLU.ltv.2024.1.1.1-512
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
1. General Information
|
Project - Project Leader
|
Project leader for scoping review Rosa Hellman, [email protected] , Department of People and Society, SLU, https://orcid.org/0009-0008-3923-3713 Project leader for Follow the food(overall project) Nicklas Neuman, [email protected] , Department of food studies, nutrition and dietetics, Uppsala University, https://orcid.org/0000-0001-7970-4753 Supervisor Sara Spendrup, [email protected] , Department of People and Society, SLU,https://orcid.org/0000-0001-7690-0919 Supervisor Jonas Bååth, [email protected] , Department of People and Society, SLU, https://orcid.org/0000-0002-9521-1833
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
1. General Information
|
Project - SLU Focus Area
|
SLU research in collaboration with Uppsala University.
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
1. General Information
|
Project - Contributor
|
Rosa Hellman, [email protected] , Department of People and Society, SLU, https://orcid.org/0009-0008-3923-3713 Nicklas Neuman, [email protected] , Department of food studies, nutrition and dietetics, Uppsala University, https://orcid.org/0000-0001-7970-4753 Sara Spendrup, [email protected] , Department of People and Society, SLU, https://orcid.org/0000-0001-7690-0919 Jonas Bååth, [email protected] , Department of People and Society, SLU, https://orcid.org/0000-0002-9521-1833
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
2. Data Description and Collection or Reuse of Existing Data
|
What type of data will be newly collected/produced and/or reused and how will new data be collected/producedand/or already existing data reused?
|
Data will be collected from already published material (re-used) . From this material new data will be generated in the form of the data types found in the table below. Data is produced through development of scoping review protocol and search strategy. The database searches will generate a specific collection of previosly published records. Screening results will generate included and excluded (with reasons) records. Data extraction will collect data from each included record. Type of data Collection of data Data format (active storage and back up) Data format (long term preservation) Data volume Search strategy(search queries & inclusion/exclusion criteria) A search strategy is developed to find relevant record in the chosen databases and inclusion/exclusion criteria to be applied when screening. .pdf and docx. .pdf <1 GB Literature database records Searches will be made in Web of Science Core Collection, Scopus, CAB Abstracts, PsychINFO and GreenFILE. RIS and .csv .csv <1 GB Records collected from other sources Reference lists of the included records from databases will be searched for additional sources. RIS and .csv .csv <1 GB Included records The screening will results in a number of included records to be used in data extraction. The citation details of these will be listed. RIS and .csv .csv <1 GB Excluded records with reason for exclusion (title/abstract level) The title/abstract screening of records will result in a number of excluded records accompanied by reason for exclusion and citation details. .csv .csv <1 GB Excluded records with reason for exclusion (full-text level) The full-text screening of records will result in a number of excluded records accompanied by reason for exclusion and citation details. .csv .csv <1 GB Data extraction of included records Data will be retrieved and reformulated from the full text of included records and transferred to the extraction sheet. The following entities will be extracted: citation details, country of study, study aims and research questions, methodological approaches, type of food waste reducing activity studied,type of connection between food retail and consumer and main study results. .xls .csv <1 GB Full text of included records Full-text of the included records will be retrieved from the databases for full- text screening. These will be kept for active storage as long as necessary for the scoping review. pdf. The possibility of publishing full-texts in SND is being looked into. <1 GB
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
2. Data Description and Collection or Reuse of Existing Data
|
In what format will the data collected/produced and/or reused come in?
|
See table under 2.2. The formats for long term preservation is based on the guidance for choosing file format provided by the the Swedish National Data Service (SND) where data will be published and from SLU Data Management Support who will assist with archiving.
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
2. Data Description and Collection or Reuse of Existing Data
|
What volume of data will be collected/produced and/or reused throughout the project's lifetime?
|
See table in 2.2.
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
3. Documentation and Data Quality
|
What metadata (i.e., contextual information describing the data) and documentation will accompany the data?
|
A readme- file (.txt) will accompany the data (see table in 2.2). This will clarify terms, abbreviations, data content, the file organisation system and the file naming convention. Descriptive, administrative, structural, provenance and access metadata will be added, based on DDI metadata standard (Data Documentation Initiative) when data is published in the SND. A scoping review protocol has been developed according to JBI Manual for Evidence Synthesis (Peters et al. 2024) and preregistered in the OSF. This will serve as a documentation of the scoping review process and published in the SND.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
3. Documentation and Data Quality
|
What data quality control measures will be used?
|
Data quality control is performed by involving at least two reviewers when screening, extracting and analysing/presenting results.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
4. Storage and Backup during the Research Process
|
How will data, metadata, and other documentation be stored and backed up during the project?
|
All data will be stored and backed up in a shared OneDrive. Raw data files are also backed up in the personal file storage system of the project administrator (file storage system provided by SLU) in read-only files.
|
8ec1441a11103ca59167b9ed660dc529
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dmponline.dcc.ac.uk
|
4. Storage and Backup during the Research Process
|
How will data be secured/protected during the project?
|
Access to data stored in OneDrive is controlled via authorisation. Back- up is handled by Microsoft. Access to data stored on personal file storage system provided by SLU is controlled via authorisation. Data recovery is ensured by SLU-IT in case of accident. As the data handled is not considered sensitive no further protection is implemented.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
5. Legal and Ethical Requirements, Codes of Conduct
|
How will compliance with legislation on personal data and on security be ensured? (multiple answers are allowed)
|
Not applicable for this project.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
5. Legal and Ethical Requirements, Codes of Conduct
|
How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation isapplicable?
|
The data will be openly accessible in SND once the review has been published in an academic journal. CC0 licencing is planned to be applied on data generated in the project. The possibility of publishing full-text of indluded records in being looked into. The publication of the scoping review in an academic journal is planned to will be openly accessible with CC BY licence.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
5. Legal and Ethical Requirements, Codes of Conduct
|
What ethical issues and codes of conduct are there, and how will they be taken into account?
|
Not applicable for this project.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
6. Data Sharing and Long-Term Preservation
|
How and when will data (or metadata) be shared (i.e., made publicly available)? Are there possible restrictions todata sharing and embargo reasons?
|
The data will be published freely accessible in the repository Swedish National Data Service (SND) after publication of the final manuscript planned within a year after project end (2024-12-13).
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
6. Data Sharing and Long-Term Preservation
|
How will data for preservation be selected, and where will data be preserved long-term (e.g., a data repository orarchive)?
|
Data will be preserved in the SND and the archive service at SLU.
|
8ec1441a11103ca59167b9ed660dc529
|
dmponline.dcc.ac.uk
|
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