frnka/dmp-qa · Datasets at Hugging Face

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Archiving/Deletion	How long will you store this information for? Where will the information be archived? How will it be destroyed?	The data will be stored for 7 years, in accordance with the University of Nottingham's policies. Following this, the data will be completely removed from where it is being stored.	adb157103ff31ea5e413e595c293f313	dmponline.dcc.ac.uk
Access to the data	List exactly who will have access to each data source.	West Mercia Police will have access to the data they provided. Sussex Police will have access to the data they provided. Dr. Kevin Browne (Principal Investigator) and Jessica Eaves (Student Investigator) will have access to both data sets.	adb157103ff31ea5e413e595c293f313	dmponline.dcc.ac.uk
Data Collection	How will the data be collected or created?	Data will be obtained through completing the questionnaires on JISC on a tablet.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Documentation and Metadata	What documentation and metadata will accompany the data?	I will supply a glossary and instructions on how to use and interpret the data alongside the dataset when uploading to Open Science Framework.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Ethics and Legal Compliance	How will you manage any ethical issues?	Participant data will remain confidential and will be stored safely in the researcher's OneDrive in a password protected folder. Participant data will also be identified using a code that the participants create themselves. Only the participant and researcher will be aware of these codes.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Ethics and Legal Compliance	How will you manage copyright and Intellectual Property Rights (IPR) issues?	The data will be the intellectual property of both the University of Bradford and UEFA.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Storage and Backup	How will the data be stored and backed up during the research?	The data will be stored safely on the researchers university OneDrive account.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Storage and Backup	How will you manage access and security?	Only the researcher and research assistant will have access to the data and this will be behind a password protected folder.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Selection and Preservation	Which data are of long-term value and should be retained, shared, and/or preserved?	This data will have strong implications for the pressure of the football from grassroots to the elite level. Therefore, the data should be retained and preserved so it is useable in subsequent studies as secondary data.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Selection and Preservation	What is the long-term preservation plan for the dataset?	This data will be available on Open Science Framework, where researchers will have access to it.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Data Sharing	How will you share the data?	This data will be available on Open Science Framework, where researchers will have access to it.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Responsibilities and Resources	Who will be responsible for data management?	Myself and the research assistant.	12d89656e1beb857df6e253cfc243fb2	dmponline.dcc.ac.uk
Responsibilities and Resources	What resources will you require to deliver your plan?	Background: Football is the world’s most popular sport with over 265 million players , in FIFA’s last census-style report in 2007 (FIFA, 2007). In England 11.8 million players participate in organised football matches and training each week (English Football Association, 2024). While regular participation in sport and physical activity has many benefits for health and well-being (Blair & Morris; 2009; Callow et al., 2020; Saxena et al., 2005), there is increasing public concern and research scrutiny regarding the long-term negative consequences of repeated ball-to-head impacts, an integral skill in football known as heading (Peek et al., 2023). To minimise the potential long-term harm associated with heading, a small number of football associations around the world have implemented Heading Guidelines. However, the evidence underpinning the strategies contained within these guidelines is unclear. Additionally, most football associations globally currently do not endorse a position on heading including the sport’s governing body, FIFA. This presents a perfect opportunity to explore the safest possible manner of heading within football’s lawmakers IFAB’s current laws. Aim: We aim to test 179 footballers (male and female) to determine whether the current boundaries of ball pressure are harmful to footballers when heading. This study could provide evidence for reducing the upper boundary, as well as identifying that these boundaries may be different in the men’s and women’s games. Methods: An experimental design will be implemented in this study. 179 footballers (both male and female) are targeted and will be randomly allocated to one of two conditions; i) heading the 9psi football ii) heading the 15psi football. Prior to the experiment, participants will complete a questionnaire that collects participant demographics and a Trail-Making Task (TMT(1)) that will record baseline data of cognitive flexibility. Next, participants will head the football back to the researcher ten times from an under-arm throw before immediately completing the TMT again (TMT(2)). The participant will then head the ball back to the researcher a further ten times from an over-arm throw before again completing the TMT (TMT(3)). Finally, participants will complete a questionnaire that will ask questions regarding the ball they have headed in the experiment. Analysis: A 2x3 MANCOVA will be used to analyse the data, where there are two levels of the independent variable ( ball pressure, 9psi vs 15 psi) and three levels of the dependent variable (cognitive flexibility, TMT(1), TMT(2), TMT(3)), with biological sex (male/female) as a covariate. Impact: From this study, we can provide evidence that we may need to reduce the guidelines on pressure of the ball, perhaps closing the gap a little closer to the lower bound. If we find a difference between the sexes, it may be that we can provide suggestions of different pressured balls between the men’s and women’s games, to ensure safety for all.	12d89656e1beb857df6e253cfc243fb2	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?	The data generated and re-used directly relate to the iRISE objectives and all protocols will transparently outline our work processes. Literature reviews will help the consortium understand the depth of literature on reproducibility definitions and metrics, and on intervention studies to improve reproducibility. The data generated in literature reviews will further efficiently train and be used by the iRISE-SOLES. Quantitative data will be re-used and gathered to answer the research questions outlined in the iRISE proposal. Qualitative data collected via Delphi method surveys, surveys, interviews and focus groups will complement existing data to gain insights on prioritisation of interventions, facilitators and barriers of implementation, and underrepresented groups. Code and other computational tools will allow us to extract the most useful information from the different data sources and represent our results adequately.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
Data Summary	What is the expected size of the data that you intend to generate or re-use?	Most data generated and re-used will be small. We expect individual data outputs (text, spreadsheets, code scripts) to stay well below 100MB in size. The SQL database for the iRISE-SOLES is expected to be at most 1GB large. The totality of the raw qualitative data might exceed this size, as it includes video and audio recordings. However, also due to sensitivity considerations, the number of recordings will be small, and the size of the data will not hinder them from being stored on the iRISE SharePoint.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
Data Summary	What is the origin/provenance of the data, either generated or re-used?	In general, the origin/provenance of each individual data output will be outlined in detail as part of our minimal metadata standards. This includes concrete workflows put in place to collect the data. Such information will also be part of the preregistered protocols. Specifically, survey data will be collected via SurveyMonkey and/or Qualtrics, the qualitative data will be aggregated in NVivo (standard software), and the bibliographic data will be annotated in open-source literature review software (SyRF or Rayyan). Two of the intervention studies (task 5.2 and 5.3) will rely on a collaboration with a publisher which will provide access to submitted manuscripts.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
Data Summary	To whom might your data be useful ('data utility'), outside your project?	All data collected during the iRISE project will be useful for any research group that intends to reproduce or build on our findings. This is particularly true for the protocols outlining our work processes and methods, the code and tools we build and make openly available, but also the data collected and aggregated in spreadsheets. More specifically, the glossary with reproducibility definitions and metrics will condense information and therefore be useful for the entire research community. The data collection of reproducibility projects and other examples will be a great resource for any other (follow-up) project in need of empirical data on reproducibility. The data collected in WP3 will be useful to inform future research projects and consortia on how to integrate equity, diversity and inclusion (EDI) dimensions. Specific iRISE data outputs are meant to help and train stakeholders to implement interventions to improve reproducibility. To further facilitate dissemination of the iRISE (data) outputs and results, some outputs will be integrated into the Embassy of Good Science .	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
FAIR data	Making data findable, including provisions for metadata: Will data be identified by a persistent identifier?	Yes, all data outputs which will be openly available will have a persistent digital object identifier (DOI). The DOI will be provided through a repository of choice (the Open Science Framework or Zenodo). Code will be version controlled via Git and linked to OSF or Zenodo to be allocated a DOI. The data that must remain closed (mentioned below) will not have a DOI.	4efb07d215cbbeee91491f8f32aad8b8	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?	Keywords will be part of the metadata provided with all the data outputs created by iRISE. Keywords on OSF are referred to as tags.	4efb07d215cbbeee91491f8f32aad8b8	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?	Whenever possible and the repository allows it, the metadata will be supplied in the repository user interface (possible for OSF and Zenodo) which ensures that the metadata is machine-readable.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
FAIR data	Making data accessible - Repository: Will the data be deposited in a trusted repository?	Yes, all data will be made accessible on trusted public data repositories. At the start of the project, iRISE created its open knowledge base on the Open Science Framework where most of the data will be uploaded (specifically, text data including protocols, reports and summaries, smaller spreadsheets, references to code on Git). Larger, more complex data will instead be deposited on Zenodo as it is more suited for complex data. Also working snapshots of Git-repositories will be deposited on Zenodo to ensure long term preservation. iRISE will create a Zenodo community to allow all consortium partners and stakeholders to have easy access to all data shared on the repository. All the iRISE data output, and work in progress will additionally be archived, versioned and secured on the iRISE SharePoint hosted by the iRISE coordinating partner (Charité). The sensitive data, mainly contact information for participants in various tasks and the raw qualitative data collected in surveys, interviews and alike, will be archived on the iRISE SharePoint and password protected. Only task leads and key individuals will have access to the password. An exception will be the contact information and data needed for the intervention study based on computational reproducibility review for which the data will stay at Karolinska Institutet, and the data collected in task 5.2 which will be stored on a separate Charité server operated by the task-leading partner. This will simplify the data transfer agreement with the publisher.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
FAIR data	Making data accessible - Repository: Have you explored appropriate arrangements with the identified repositorywhere your data will be deposited?	The Open Science Framework is a free and reliable service. For example, the OSF FAQs explain that “the OSF database is backed up via streaming replication 24 hours a day, and incremental restore points are made twice daily”. Already at the time of proposal submission, the iRISE partners created an OSF project which will now act as the iRISE open knowledge base. Additionally, larger and more complex data will be uploaded on Zenodo, as it is a more appropriate repository to deal with complex data. Working snapshots of the iRISE Git-repositories will be uploaded to Zenodo to ensure long term storage of the code and software produced. Zenodo is another free service with easy access to all partners and all interested in the project and its outputs. Zenodo also has a comprehensive strategy for longevity of the data uploaded. We will create a Zenodo community to ensure all generated data is connected to iRISE and easily findable and identifiable as such. Finally, another free and accessible repository or service used is Git. To increase findability, all Git-repositories will be linked to the iRISE open knowledge base on OSF. Linking to OSF and/or uploading a snapshot to Zenodo also allows the code and software to have a DOI.	4efb07d215cbbeee91491f8f32aad8b8	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, OSF and Zenodo allow the data to be assigned a persistent digital object identifier (DOI). Git does not provide this service. However, all iRISE Git-repositories will be linked to from the iRISE open knowledge base on OSF or Zenodo, which allows the code and software generated to have a DOI. Working snapshots of the iRISE Git-repositories will also be assigned a DOI via Zenodo.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
FAIR data	Increase data re-use:Will the data produced in the project be useable by third parties, in particular after the end of the project?	Yes, all open data will remain on the selected repository and any third party will have access, also after completion of the project (for as long as the repositories operate).	4efb07d215cbbeee91491f8f32aad8b8	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.).	All questions answered above already refer to other research outputs since they are part of our iRISE data output definition.	4efb07d215cbbeee91491f8f32aad8b8	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.	This was already answered above.	4efb07d215cbbeee91491f8f32aad8b8	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.) ?	Most of the data can be made openly available and as FAIR as possible without any cost related to storage, archiving, etc. Much of the data will be backed-up on the iRISE SharePoint hosted by Charité, which will also be without cost for the entirety of the project and beyond. For some specific tasks, other institutional servers will be used free of charge to facilitate data transfer agreements. To keep the iRISE-SOLES running a team member needs to regularly check that the scripts and cloud computing infrastructure are running. The workload here is estimated to be about two hours per month. Additionally, the SQL database will be running on Amazon web services, which is subject to a fee.	4efb07d215cbbeee91491f8f32aad8b8	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)	The costs to keep the iRISE-SOLES running after the completion of iRISE will be absorbed by the responsible partner (University of Edinburgh), which has several other SOLES and databases running and hosted on Amazon web services/shinyapps.io. The fees will be part of the partner's budget after project completion.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
Allocation of resources	Who will be responsible for data management in your project?	The task lead will be responsible for the management of the data generated by their task. The WP leads are responsible for keeping an eye on data management. The authors of this DMP (R. Heyard and G. Currie) oversee the iRISE data management and can be consulted for help if necessary.	4efb07d215cbbeee91491f8f32aad8b8	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)?	The iRISE consortium plans to forge partnerships with stakeholders who use the iRISE-SOLES platform to support this effort in the long term. All underlying code for the user interface will be openly available to ensure a copy can be generated at any time if an issue were to affect the continuity of the platform. For all other data output, the long-term preservation will be facilitated through the respective data repositories (OSF, Zenodo). Specifically, since Git does not have a strategy for the long-term preservation, all Git- repositories will have a working snapshot uploaded to Zenodo.	4efb07d215cbbeee91491f8f32aad8b8	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 outputs will be backed-up on the iRISE SharePoint to which only registered iRISE partners have access. The sensitive data will be deposited on the iRISE SharePoint and will be encrypted, with the encryption key also on the SharePoint. The access to this data, specifically to the encryption key, will be strictly controlled (e.g., only partners involved in the specific tasks will have access). For the tasks collaborating with publishers, data transfer agreements will outline the storage and access in detail. The iRISE-SOLES SQL database will be backed-up regularly and snapshots will be uploaded to OSF and made openly available.	4efb07d215cbbeee91491f8f32aad8b8	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).	Section 4. Ethics self-assessment of the iRISE Description of the Action discusses ethical dimensions and the compliance with ethical principles in detail. Some tasks, analyses and studies planned in iRISE will need ethical approval. This includes the qualitative and mixed-methods research planned by WP4. The strategy for ethics approval will be discussed in the respective protocols. The same is true for some of the intervention studies planned in WP5 (task 5.2, task 5.3 and 5.4). Overall, any ethics and/or legal considerations required were discussed above, e.g., in relation to sensitive data.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
Ethics	Will informed consent for data sharing and long term preservation be included in questionnaires dealing with personaldata?	Whenever personal data is collected in questionnaires or alike, participants will be informed on what types of data is collected and how and for how long the data will be preserved. For example, informed consent documentation will be created for the Delphi consultation process and focus group discussion in WP4. Further considerations will be outlined in the ethics sections of the protocols for the studies collecting personal data.	4efb07d215cbbeee91491f8f32aad8b8	dmponline.dcc.ac.uk
1. Data description	Describe how data will be collected, created or reused.	Semi-structured interviews using vignette methods will be carried out with professionals, working in diverse organizational settings, such as NGOs and CSO with the aim to understand their professional practices in cases involving migrant women having a precarious legal status and experiences of gender-based violence. Interviews are carried out in different ways. Interviews can be physical meetings and recorded using a device, or else can be done digitally using zoom, and registered using the zoom option/institutional laptop. Professionals who want to take part in the study but do not want to do interviews can choose to respond to a questionnaire. The questionnaire is generated through Sunet Survey from Lund University System.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
1. Data description	What type of material (physical or digital) will you use (e.g. text, images, measurement data)? In which fileformats will you save your data?	All the interviews will be digitally recorded and saved as audio files. No name will be attached to the files, but every file corresponds to a code number assigned depending on the dates and time of the interview. The list of files will be backup in two USBs stored in a safe at Lund University, Department of Social Work. Code keys and personal data shall be kept apart and protected by a password- protected computer or server, or in a safety cabinet. Audio recordings from the interviews are also added to the PULU database. see point 4.1.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
1. Data description	According to your estimation, how large is the maximum storage capacity will you need throughout the project(primary data and revisions of processed data)?	The total amount of interviews will be up to 30 or 40.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
2. Documentation and data quality	How will your data/your material be documented and described with metadata, take collection method, content,structure, standards and formats in consideration; in order for you and other researchers or computer software toread and be able to interpret the data correctly?	The data will be transcribed into Word files who will be saved using an ordinal numeric number. All the files will be imported into MAXQDA Software and analyzed through qualitative methods, which means quotations and texts will be interpreted by the researcher and categorized in codes, and sub-codes, then grouped into themes. Text analysis could be also performed. Variables such as the size of the organizations, the main activities of the organizations, the type of human resources of the organizations, and the financial resources of the organizations in which the different respondents' work might be used during the analysis of the data. However, no references will be made to names of the cities, organization names, and any other details that could identify the workplace of the respondents. This means that also the variables described above and related to the organizations where the respondents' work will be grouped and anonymized.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
2. Documentation and data quality	How will the quality of the research data be ensured and documented (for example by repeated measurements,data entry validation etc.)?	Qualitative analysis using QDAS software ensures the reliability and validity of the analysis. Reflexivity of the researcher and repeated discussions with peers and senior colleagues will help the researcher to validate the results and findings.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
3. Storage and backup	How will you ensure integrity of storage and backup of data and metadata during the research process?	All the software I used has a license for the protection of the data. The use of Lund University systems reinforces data protection. Furthermore, all data collected and analyzed are anonymized. The files with the audio recording and the transcripts will be stored in a USB and put in a safe available in my department at Lund University. During the analysis of the data, the files generated will be saved as well in a USB and only processed through the institutional laptop.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
3. Storage and backup	How is information security and access to data controlled, for example in reference to sensitive data and personaldata?	Participation in the interviews/questionnaire is completely voluntary. All respondents have been informed about the purpose of the study, the methods used, and the data storage. All participants consent to record the data. All data are anonymized, and it is impossible to retrace any personal information protected under the GDPR. Thus, neither in the audio recording nor in the transcript is there a reference to names of participants, age, gender, race/ethnicity, and any other personal information. The access to data is also controlled together with my two supervisors. They both have access to the safe in which data are stored. We all work at Lund University, at the Department of Social Work, Faculty of Social Sciences, and work through the LU system, which ensures data protection.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
4. Legal and ethical requirements	How will you ensure that data is processed according to the regulations concerning for example personal recordhandling, confidentiality and intellectual property rights?	In this study, we follow the principle of "do no harm". In relation to respecting the dignity of participants, the researcher through a reflexive process chose carefully the methodology to be used in the study. The principle of "reciprocity" guided the process of sampling, which means the researcher gave the possibility to all the participants to use their "native language". All the material was translated and accessed in three languages, which are Swedish, English, and Italian. All participants were also informed about the methodology and were asked if they want to stay updated about the project and the results. Participants were all qualified informants, experts in their specific professional fields, and they were all discussing "the system" and their professional practices with no reference to any individual or specific identifiable situation. Participation is voluntary and all participants could drop out at any time or they were informed that at any moment they could decide to access or remove their own data from the project. Anonymity was ensured using numerical codes attached to each and every file related to interviews. Variables referring to organizational information were stored separately in an additional excel file.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
4. Legal and ethical requirements	In what way will you ensure that data is handled correctly from an ethical standpoint?	The Swedish Ethical Review Act is applied when it involves the processing of sensitive personal data as defined by the GDPR or involves the processing of sensitive personal data regarding violations of law that includes crimes, judgments in criminal cases, penal law sanctions, or administrative deprivation of liberty. Sensitive personal data (känsliga personuppgifter), or ”special categories of personal data” (GDPR), are defined as those data that reveal: • race or ethnic origin • political opinions • religious or philosophical beliefs • trade union membership • sex life or sexual orientation • health • genetic information • biometric information for the purpose of uniquely identifying a person None of the data described above is present in my dataset and no ethical review has been submitted. However, this study complies with the ethical guidelines for research, data protection, and processing detailed in The European Code of Conduct for Research Integrity (https://ec.europa.eu/ research/participants/data/ref/h2020/other/hi/h2020-ethics_code-of- conduct_en.pdf), which means that ethical principles were applied in all the steps of the research.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
5. Data sharing and long-term preservation	Will research data and/or information on data (metadata) be made publicly available?	The deliverables of my Ph.D. projects are scientific articles, conference papers, and popular scientific writings which means that metadata will be shared and discussed with the informed public such as editors and reviewers of scientific journals but only on demand. Data will be anonymized.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
5. Data sharing and long-term preservation	If so, how, when and where will data and/or metadata be made available? Are there any limitations (legal and/orethical) that prevents sharing or reuse of it?	Metadata in the form of funding and dissertation books will be sent to all the participants who showed interest and consent to access the results. A copy of the dataset containing all the material used by the researcher will be stored in a USB in a secured safe/locker at my department.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
5. Data sharing and long-term preservation	If you plan to make data/metadata publicly available, will you use a unique and persistent identifier (PID) such asa DOI?	I do not plan to use a DOI for my dataset, which means that the data recorded will not be publicly available in any platform or service.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
6. Responsibilities and resources	Who is responsible for the data management and assists with the data management during the project? Who isresponsible for data management, keeping of records and long-term preservation, after the project finished?	The principal investigator (PI) is responsible for the DMP. The two supervisors will assist the PI. Norma Montesino (supervisor) [email protected] Roberto Scaramuzzino (supervisor) [email protected] Claudia Di Matteo (PhD student) [email protected]	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
6. Responsibilities and resources	What resources (cost, labor and miscellaneous costs) will be allocated to data management (including storage,backup, data sharing and long-time preservation preparation) within the project?	I have access to a safe that my department provides.	3f16ba8a6b2f8a1165f01af61655cb0a	dmponline.dcc.ac.uk
0. Administrative questions	Name of data management support staff consulted during the preparation of this plan.	My faculty data steward, Richard Grimes, has reviewed this DMP on 06 October 2023.	02c8e631c7f964c12ce21c030c1f0226	dmponline.dcc.ac.uk
I. Data description and collection or re-use of existing data	How much data storage will you require during the project lifetime?	We expect to interview at most 20 participants. The size of each recording depends on the quality format of the recording.	02c8e631c7f964c12ce21c030c1f0226	dmponline.dcc.ac.uk
IV. Legal and ethical requirements, codes of conduct	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. If this is not the case, you can use the example below. The recordings will only be listened to by the researchers working on this project. The recoding will be transcribed, after which they will be deleted. The transcriptions will be annonymised. The datasets underlying the published papers will not be publicly released. During the active phase of research, the project leader from TU Delft will oversee the access rights to data and other outputs. The datasets will not be released publicly at the time of publication of corresponding research papers. The participation in this study is entirely voluntary, the participants have the right to withdraw at any moment during the interview, in which case we remove their data. The partipicants can withdraw their data until 31st December. After this period the data has been already processed and incorporated in scientific articles. The published materials (thesis, article) will contain statistics about the collected data and insights derived from the data.	02c8e631c7f964c12ce21c030c1f0226	dmponline.dcc.ac.uk
IV. Legal and ethical requirements, codes of conduct	Please list the categories of data subjects	Data professionals: data analysts, data engineers, data scientists outside of TUD organisation.	02c8e631c7f964c12ce21c030c1f0226	dmponline.dcc.ac.uk
IV. Legal and ethical requirements, codes of conduct	Please describe the informed consent procedure you will follow:	Signatures __________________________ _________________________ ________ Name of participant Signature Date	02c8e631c7f964c12ce21c030c1f0226	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 resulting from this project?	Asterios Katsifodimos [email protected] Assistant Professor in Web Information Systems research group. Fenia Aivaloglou [email protected] Assistant Professor in Web Information Systems research group.	02c8e631c7f964c12ce21c030c1f0226	dmponline.dcc.ac.uk
General information	Title of the project	SPR Biotichs data management plan	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
General information	Short summary of the data collection	SPR Biotichs increases knowledge and operational approaches for the transition to a sustainable living environment, restoration of nature and biodiversity and sustainable use of ecosystem services.  We collect data for the data lake on organisms (terrestrial, aquatic and subterranean), environmental pressure factors, toxins, soil ecosystem services. This data is converted to a uniform spatiotemporal scale. The data is subsequently combined highlighting data gaps where data points are missing. Improving the data using machine learning techniques will fill these data gaps. Allowing for analysis of the data. Final data products are in accordance with the FAIR principles.	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
General information	What is the aim of the data?	Combining this data will allow for regional assessment of effects of environmental pressure factors on biodiversity and the effect this biodiversity has on ecosystem services.	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
Project plan	Reference to the project plan	see SPR Biotichs project plan	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
Project plan	Location of the project plan	SPR Biotichs project folders teams	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
Contributor(s) and reviewer(s) of DMP	Author(s) of the DMP	Schoffelen, N; Hofman, T; Zwaan, R; Spijker, J.	df46eac438851de7508060f899c99134	dmponline.dcc.ac.uk
1. Project details	Unique researcher ID:	0000-0002-1719-8573	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
1. Project details	Provisional project title:	NTU CCJ - Departmental Culture Survey	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
1. Defining your data	Describe your data and how you will be working with it	A single point of distribution will lead participants to a survey designed to capture the key aspects of cultural experience, alongside key demographic information. The data collection will use Microsoft Forms, but with two separate surveys that will move seamlessly from one to the other. This will enable participants to give detailed qualitative commentary without any risk of back-identification through triangulation of quantitative data. A 28 item quantitative survey will be used to assess the key variables through questions that have been devised by Athena Swan and allow us to cover the core areas of concern along with potentially benchmarking against other institutions or departments. This survey will also contain questions on gender (using Athena Swan's recommended format), job role at a broad enough level, and an additional question regarding self-identified aspects of marginalised social identity. The qualitative survey will ask participants a single open question for each of the six themes covered by the 28 questions in the Athena Swan survey. Through using Microsoft Forms, the data will be collected within the university's secure systems at the outset. On an annual basis, the data will be downloaded into files stored within the university's IT infrastructure, and deleted from the original Forms system to maintain control. The quantitative data will be stored in a single Excel file to be held within a sharepoint accessible to members of the departmental Athena Swan committee to be formed in January 2024 (to be held in an departmental Athena Swan sharepoint). The qualitative data will be stored in the OneDrive of the survey owner and shared only with appropriate key members of the management team, due to the higher risk of confidentiality breaches inherent in such data.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
1. Defining your data	What formats and software will you use?	Microsoft Forms will be used to collect the data. The extract is naturally in Microsoft Excel format.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
1. Defining your data	How much data do you expect to generate?	Annually, the maximum data size will be dependent upon size of staff and doctoral candidate group. With a relatively small text- based dataset, the size is unlikely to ever exceed 100MB annually.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
2. Compliance & data ownership	What do you need to do to comply with these obligations?	We are collecting personally sensitive data in the form of gender identity. To maintain GDPR compliance, we will collect the data in a fully anonymised manner from the outset. Data will never leave the NTU IT infrastructure, in which password control and encryption is already automatically at an appropriate standard to comply with the policies above. The purpose of this data is purely for this evaluative project, and will not be made available for any other purpose. The project is a service evaluation rather than academic research - we do not intend to make general statements about the human condition as a result of this data. As such, the requirement to retain data for academic scrutiny is not aligned. Rather, we will retain the data within our secure sharepoint area in order for us to allow further comparative analysis over an appropriate period. This period will be aligned with the Athena Swan submission life cycle - for example, in the current form, this would mean that data collected in 2024 will be retained until 2030 to ensure that it can be appropriately compared as part of the Athena SWAN submission in 2030, but this retention period will change should the rules around Athena Swan submissions change in the meantime. Once used for an Athena Swan submission, data older than three years will be deleted, other than the final data set to ensure that a full period of comparison can always be reported. Participants will be directed to read this data management plan at the start of the survey, if they wish to know about how we are managing the data.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	Where will you store your data?	As noted above, the data is maintained entirely within NTU's IT infrastructure from collection through to management and storage.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	How will you back-up your data?	Data back-up will occur through NTU's existing processes for backing up data with Microsoft Sharepoint space	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	Who else is allowed to access this data during the project?	During collection, the data will be stored on the Microsoft Forms systems. Only the project lead (an Associate Professor with a portfolio of EDI responsibilities that include departmental culture) and the Head of Department (tbc) will have access to the raw qualitative data, with another senior colleague with EDI leadership role also having access to the quantitative data. This is to ensure we have contingency access should unforseen circumstances remove the project lead, without over-extending the raw access. On an annual basis, the data will be downloaded into files stored within the university's IT infrastructure, and deleted from the original Forms system to maintain control. The quantitative data will be stored in a single Excel file to be held within a sharepoint accessible to members of the departmental Athena Swan committee to be formed in January 2024 (to be held in an departmental Athena Swan sharepoint). The qualitative data will be stored in the OneDrive of the survey owner and shared only with appropriate key members of the management team, due to the higher risk of confidentiality breaches inherent in such data.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	How will you organise your data folders?	As there will be just two excel files, no folder management is required. Within the quantitative excel file, a new tab will be created to store the raw data for each year. This will allow for any manipulation required should subsequent amendments be made in response to Athena SWAN directives in future, before transfer into a "Full Data" tab with one row per response and the additional variable of "Year" added.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	How will you name your files?	-NTU CCJ Culture Survey (Quantitative) -NTU CCJ Culture Survey (Qualitative)	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	How will you manage different versions of your files?	As noted above. There should not be different versions of the files required.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
3. Working with your data	How will you ensure your data is understandable to others?	A copy of the survey questions will be stored alongside the the data files.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
4. Archiving your data	What data should be kept, or destroyed, after the end of your project?	As described above, the Athena Swan process is a live one, requiring a constant cycle of review and action. As such, the data quantitative data underpinning our work - including the ongoing comparisons with previous data - remain live until there has been a further departmental submission. Cumulative years of data are considered to active data for storage as outlined above, as such data will continue to be regularly accessed until the submission of the departmental Athena SWAN re-accreditation request. Once such submission is made, the active data will revert to only the two most recent years, with previous years' data deleted permanently from the file. Two years are maintained so that the next survey run can allow for a three-year trend to be reported; once achieved, the oldest year of data will also be deleted, so that the active dataset continues to only hold the data from the point of last submission, and then all subsequent years leading up to the next submission. In this context, there will be no formal archiving process required or undertaken. For the qualitative data, once the annual report on the data has been produced, shared with the CCJ Leadership team, and considered complete by that team, the data will be destroyed.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
5. Sharing your data	How will others learn that your data exists?	This data is for evaluation and not research, and is therefore outside of the normal academic imperatives to make publicly available and known. Furthermore, it is important for staff to answer on the understanding that it is for evaluation only and will not be repurposed for broader academic purposes. On this basis, the data will not be shared beyond the immediate project group, and there are no plans to make it possible for others to learn of continued data existence.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
5. Sharing your data	Which data will be accessible to others?	Quantitative: None - only the immediate project team will have access to the active data as defined above. Qualitative: Extracts may be used in reports that will be available within NTU annually, and subsequently in Athena SWAN submissions. Raw data will only be available to project lead and Head of Department whilst it remains active.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
5. Sharing your data	How will you share your data?	See above. CCJ Athena Swan Committee will have access by virtue of their managed access to the Teams sharepoint where data will be managed following export.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
6. Implementing your DMP	How often will this plan be reviewed and updated?	The project team will review this plan annually, prior to the annual collection. Amendments required will be shared with departmental leadership as appropriate.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
6. Implementing your DMP	What actions have you identified from the rest of this plan?	All actions already in place.	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
6. Implementing your DMP	What support/ information do you need to complete these actions?	No further support required	a34d3d429cbd7057f5c0713e3812f439	dmponline.dcc.ac.uk
Manchester Data Management Outline	Who will act as the data custodian for this study, and so be responsible for the information involved?	N.A. - theoretical project	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Project details	What is the purpose of your research project?	To analyse marginalisation in democracy	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Project details	What policies and guidelines on data management, data sharing, and data security are relevant to your researchproject?	None - theoretical project	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Data Collection	What data will you collect or create?	I will read things and write about what I've read	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Data Collection	How will the data be collected or created?	On my computer, and in whatever written notes I produce	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Ethics and Legal Compliance	How will you manage any ethical issues?	I don't see how reading/writing about published works will be ethically concerning	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Ethics and Legal Compliance	How will you manage copyright and Intellectual Property Rights (IPR) issues?	I will cite the authors I've read when referencing their arguments/findings	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Storage and backup	How will the data be stored and backed up?	On a computer	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
Storage and backup	How will you manage access and security?	Passwords	45682e592984481d2afc5dd83c0427c2	dmponline.dcc.ac.uk
1. General features	Acronym/short study title	Acronym for the study needs to be determined	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
1. General features	Path of the Research Folder	\\ds\data\Beeld\ResearchFolders	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
1. General features	ABR number (only for human-related research)	ABR nummer moet nog verkregen worden	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
1. General features	METC number (only for human-related research)	METC nummer moet nog verkregen worden	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
1. General features	Name of datamanager consulted	Rogier Schokker	24adb1188d97f228d76f9625fd6e2647	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 222 EPD Castor Quantitative .csv Human 222 Blood samples Castor Quantitative .csv	24adb1188d97f228d76f9625fd6e2647	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>	Informed consent will be obtained from all patients in the study after the diagnosis of HPV-related oropharyngeal cancer. Clinical patient data (including information on disease residue or recurrence) and pathological tumor characteristics will be collected from the EPD and added in a Castor database. A total of 11 blood samples will be collected from every patient (once before the start of treatment and 10 times after the end of treatment for a duration of 2 years). These blood samples will be send pseudononymized to the UMCU and MUMC+. Here, plasma isolation, storage, and the detection of circulating HPV DNA will take place. The presence and quantitiy (number of DNA copies) of circulating HPV DNA in the blood plasma will be added to the corresponding patient in the Castor database. The data flow in the analsyis phase for the clinical data will be: Clinical data from EPD > entered in Castor EDC > export in SAS software > analysis in SPSS software and/or R software The data flow in the analsyis phase for the blood sample derived data will be: Blood sample > transport to MUMC+ or UMCU > analysis by molecular diagnostic lab, pathology department > output entered in Castor EDC > export in SAS software > analysis in SPSS software and/or R software.	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
2. Data Collection	Estimated storage space for your project	Data also inclused raw data (including analysis plots) of all performed ddPCR experiments.	24adb1188d97f228d76f9625fd6e2647	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. Time of datamanager X 2. Castor Database RVB/UMCU 3. Storage X 4. Archiving X 5. Explanation: 3. License fee for using Castor 5. Where data will be archived and how these costs will be covered has yet to be determined. This answer will be updated later.	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
2. Data Collection	Please give some more details on other centers involved. What are the roles of the other centers involved? (whatresearch activity does this organization carry out in relation to the study and the data?)	Organization Role/research activity MUMC+ Participating center (patient inclusion, blood sample measurements and storage, analysis) Julius Clinical Monitoring, statistics, HTA analysis UMCG Participating center MC Leeuwarden Participating center LUMC Participating center Haaglanden Participating center Radboud UMC Participating center Rijnstate Participating center Erasmus MC Participating center MST Enschede Participating center ETZ Tilburg Participating center NKI-AVL Participating center	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
2. Data Collection	Which contracts are in place?	Organization Contract Type with UMCU MUMC+ Research Collaboration Agreement UMCG Consortium agreement, MTA MC Leeuwarden Consortium agreement, MTA LUMC Consortium agreement, MTA Haaglanden Consortium agreement, MTA Radboud UMC Consortium agreement, MTA Rijnstate Consortium agreement, MTA Erasmus MC Consortium agreement, MTA MST Enschede Consortium agreement, MTA ETZ Tilburg Consortium agreement, MTA NKI-AVL Consortium agreement, MTA	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk
2. Data Collection	State how ownership of the data and intellectual property rights (IPR) to the data will be managed	UMC Utrecht and MUMC+ are and remain the owner of all collected data for this study. The data is collected in a relatively large patient group and is very valuable for further, broader studies in Europe. Our data cannot be protected with IPR, but its value will be taken into account when making our data available to others, when setting up Research Collaborations and when drawing up Data Transfer Agreement(s).	24adb1188d97f228d76f9625fd6e2647	dmponline.dcc.ac.uk

Archiving/Deletion

How long will you store this information for? Where will the information be archived? How will it be destroyed?

The data will be stored for 7 years, in accordance with the University of Nottingham's policies. Following this, the data will be completely removed from where it is being stored.

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dmponline.dcc.ac.uk

Access to the data

List exactly who will have access to each data source.

West Mercia Police will have access to the data they provided. Sussex Police will have access to the data they provided. Dr. Kevin Browne (Principal Investigator) and Jessica Eaves (Student Investigator) will have access to both data sets.

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dmponline.dcc.ac.uk

Data Collection

How will the data be collected or created?

Data will be obtained through completing the questionnaires on JISC on a tablet.

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dmponline.dcc.ac.uk

Documentation and Metadata

What documentation and metadata will accompany the data?

I will supply a glossary and instructions on how to use and interpret the data alongside the dataset when uploading to Open Science Framework.

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dmponline.dcc.ac.uk

Ethics and Legal Compliance

How will you manage any ethical issues?

Participant data will remain confidential and will be stored safely in the researcher's OneDrive in a password protected folder. Participant data will also be identified using a code that the participants create themselves. Only the participant and researcher will be aware of these codes.

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dmponline.dcc.ac.uk

Ethics and Legal Compliance

How will you manage copyright and Intellectual Property Rights (IPR) issues?

The data will be the intellectual property of both the University of Bradford and UEFA.

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dmponline.dcc.ac.uk

Storage and Backup

How will the data be stored and backed up during the research?

The data will be stored safely on the researchers university OneDrive account.

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dmponline.dcc.ac.uk

Storage and Backup

How will you manage access and security?

Only the researcher and research assistant will have access to the data and this will be behind a password protected folder.

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dmponline.dcc.ac.uk

Selection and Preservation

Which data are of long-term value and should be retained, shared, and/or preserved?

This data will have strong implications for the pressure of the football from grassroots to the elite level. Therefore, the data should be retained and preserved so it is useable in subsequent studies as secondary data.

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dmponline.dcc.ac.uk

Selection and Preservation

What is the long-term preservation plan for the dataset?

This data will be available on Open Science Framework, where researchers will have access to it.

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dmponline.dcc.ac.uk

Data Sharing

How will you share the data?

This data will be available on Open Science Framework, where researchers will have access to it.

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Responsibilities and Resources

Who will be responsible for data management?

Myself and the research assistant.

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Responsibilities and Resources

What resources will you require to deliver your plan?

Background: Football is the world’s most popular sport with over 265 million players , in FIFA’s last census-style report in 2007 (FIFA, 2007). In England 11.8 million players participate in organised football matches and training each week (English Football Association, 2024). While regular participation in sport and physical activity has many benefits for health and well-being (Blair & Morris; 2009; Callow et al., 2020; Saxena et al., 2005), there is increasing public concern and research scrutiny regarding the long-term negative consequences of repeated ball-to-head impacts, an integral skill in football known as heading (Peek et al., 2023). To minimise the potential long-term harm associated with heading, a small number of football associations around the world have implemented Heading Guidelines. However, the evidence underpinning the strategies contained within these guidelines is unclear. Additionally, most football associations globally currently do not endorse a position on heading including the sport’s governing body, FIFA. This presents a perfect opportunity to explore the safest possible manner of heading within football’s lawmakers IFAB’s current laws. Aim: We aim to test 179 footballers (male and female) to determine whether the current boundaries of ball pressure are harmful to footballers when heading. This study could provide evidence for reducing the upper boundary, as well as identifying that these boundaries may be different in the men’s and women’s games. Methods: An experimental design will be implemented in this study. 179 footballers (both male and female) are targeted and will be randomly allocated to one of two conditions; i) heading the 9psi football ii) heading the 15psi football. Prior to the experiment, participants will complete a questionnaire that collects participant demographics and a Trail-Making Task (TMT(1)) that will record baseline data of cognitive flexibility. Next, participants will head the football back to the researcher ten times from an under-arm throw before immediately completing the TMT again (TMT(2)). The participant will then head the ball back to the researcher a further ten times from an over-arm throw before again completing the TMT (TMT(3)). Finally, participants will complete a questionnaire that will ask questions regarding the ball they have headed in the experiment. Analysis: A 2x3 MANCOVA will be used to analyse the data, where there are two levels of the independent variable ( ball pressure, 9psi vs 15 psi) and three levels of the dependent variable (cognitive flexibility, TMT(1), TMT(2), TMT(3)), with biological sex (male/female) as a covariate. Impact: From this study, we can provide evidence that we may need to reduce the guidelines on pressure of the ball, perhaps closing the gap a little closer to the lower bound. If we find a difference between the sexes, it may be that we can provide suggestions of different pressured balls between the men’s and women’s games, to ensure safety for all.

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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?

The data generated and re-used directly relate to the iRISE objectives and all protocols will transparently outline our work processes. Literature reviews will help the consortium understand the depth of literature on reproducibility definitions and metrics, and on intervention studies to improve reproducibility. The data generated in literature reviews will further efficiently train and be used by the iRISE-SOLES. Quantitative data will be re-used and gathered to answer the research questions outlined in the iRISE proposal. Qualitative data collected via Delphi method surveys, surveys, interviews and focus groups will complement existing data to gain insights on prioritisation of interventions, facilitators and barriers of implementation, and underrepresented groups. Code and other computational tools will allow us to extract the most useful information from the different data sources and represent our results adequately.

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dmponline.dcc.ac.uk

Data Summary

What is the expected size of the data that you intend to generate or re-use?

Most data generated and re-used will be small. We expect individual data outputs (text, spreadsheets, code scripts) to stay well below 100MB in size. The SQL database for the iRISE-SOLES is expected to be at most 1GB large. The totality of the raw qualitative data might exceed this size, as it includes video and audio recordings. However, also due to sensitivity considerations, the number of recordings will be small, and the size of the data will not hinder them from being stored on the iRISE SharePoint.

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dmponline.dcc.ac.uk

Data Summary

What is the origin/provenance of the data, either generated or re-used?

In general, the origin/provenance of each individual data output will be outlined in detail as part of our minimal metadata standards. This includes concrete workflows put in place to collect the data. Such information will also be part of the preregistered protocols. Specifically, survey data will be collected via SurveyMonkey and/or Qualtrics, the qualitative data will be aggregated in NVivo (standard software), and the bibliographic data will be annotated in open-source literature review software (SyRF or Rayyan). Two of the intervention studies (task 5.2 and 5.3) will rely on a collaboration with a publisher which will provide access to submitted manuscripts.

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dmponline.dcc.ac.uk

Data Summary

To whom might your data be useful ('data utility'), outside your project?

All data collected during the iRISE project will be useful for any research group that intends to reproduce or build on our findings. This is particularly true for the protocols outlining our work processes and methods, the code and tools we build and make openly available, but also the data collected and aggregated in spreadsheets. More specifically, the glossary with reproducibility definitions and metrics will condense information and therefore be useful for the entire research community. The data collection of reproducibility projects and other examples will be a great resource for any other (follow-up) project in need of empirical data on reproducibility. The data collected in WP3 will be useful to inform future research projects and consortia on how to integrate equity, diversity and inclusion (EDI) dimensions. Specific iRISE data outputs are meant to help and train stakeholders to implement interventions to improve reproducibility. To further facilitate dissemination of the iRISE (data) outputs and results, some outputs will be integrated into the Embassy of Good Science .

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dmponline.dcc.ac.uk

FAIR data

Making data findable, including provisions for metadata: Will data be identified by a persistent identifier?

Yes, all data outputs which will be openly available will have a persistent digital object identifier (DOI). The DOI will be provided through a repository of choice (the Open Science Framework or Zenodo). Code will be version controlled via Git and linked to OSF or Zenodo to be allocated a DOI. The data that must remain closed (mentioned below) will not have a DOI.

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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?

Keywords will be part of the metadata provided with all the data outputs created by iRISE. Keywords on OSF are referred to as tags.

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FAIR data

Making data findable, including provisions for metadata: Will metadata be offered in such a way that it can beharvested and indexed?

Whenever possible and the repository allows it, the metadata will be supplied in the repository user interface (possible for OSF and Zenodo) which ensures that the metadata is machine-readable.

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FAIR data

Making data accessible - Repository: Will the data be deposited in a trusted repository?

Yes, all data will be made accessible on trusted public data repositories. At the start of the project, iRISE created its open knowledge base on the Open Science Framework where most of the data will be uploaded (specifically, text data including protocols, reports and summaries, smaller spreadsheets, references to code on Git). Larger, more complex data will instead be deposited on Zenodo as it is more suited for complex data. Also working snapshots of Git-repositories will be deposited on Zenodo to ensure long term preservation. iRISE will create a Zenodo community to allow all consortium partners and stakeholders to have easy access to all data shared on the repository. All the iRISE data output, and work in progress will additionally be archived, versioned and secured on the iRISE SharePoint hosted by the iRISE coordinating partner (Charité). The sensitive data, mainly contact information for participants in various tasks and the raw qualitative data collected in surveys, interviews and alike, will be archived on the iRISE SharePoint and password protected. Only task leads and key individuals will have access to the password. An exception will be the contact information and data needed for the intervention study based on computational reproducibility review for which the data will stay at Karolinska Institutet, and the data collected in task 5.2 which will be stored on a separate Charité server operated by the task-leading partner. This will simplify the data transfer agreement with the publisher.

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FAIR data

Making data accessible - Repository: Have you explored appropriate arrangements with the identified repositorywhere your data will be deposited?

The Open Science Framework is a free and reliable service. For example, the OSF FAQs explain that “the OSF database is backed up via streaming replication 24 hours a day, and incremental restore points are made twice daily”. Already at the time of proposal submission, the iRISE partners created an OSF project which will now act as the iRISE open knowledge base. Additionally, larger and more complex data will be uploaded on Zenodo, as it is a more appropriate repository to deal with complex data. Working snapshots of the iRISE Git-repositories will be uploaded to Zenodo to ensure long term storage of the code and software produced. Zenodo is another free service with easy access to all partners and all interested in the project and its outputs. Zenodo also has a comprehensive strategy for longevity of the data uploaded. We will create a Zenodo community to ensure all generated data is connected to iRISE and easily findable and identifiable as such. Finally, another free and accessible repository or service used is Git. To increase findability, all Git-repositories will be linked to the iRISE open knowledge base on OSF. Linking to OSF and/or uploading a snapshot to Zenodo also allows the code and software to have a DOI.

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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, OSF and Zenodo allow the data to be assigned a persistent digital object identifier (DOI). Git does not provide this service. However, all iRISE Git-repositories will be linked to from the iRISE open knowledge base on OSF or Zenodo, which allows the code and software generated to have a DOI. Working snapshots of the iRISE Git-repositories will also be assigned a DOI via Zenodo.

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FAIR data

Increase data re-use:Will the data produced in the project be useable by third parties, in particular after the end of the project?

Yes, all open data will remain on the selected repository and any third party will have access, also after completion of the project (for as long as the repositories operate).

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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.).

All questions answered above already refer to other research outputs since they are part of our iRISE data output definition.

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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.

This was already answered above.

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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.) ?

Most of the data can be made openly available and as FAIR as possible without any cost related to storage, archiving, etc. Much of the data will be backed-up on the iRISE SharePoint hosted by Charité, which will also be without cost for the entirety of the project and beyond. For some specific tasks, other institutional servers will be used free of charge to facilitate data transfer agreements. To keep the iRISE-SOLES running a team member needs to regularly check that the scripts and cloud computing infrastructure are running. The workload here is estimated to be about two hours per month. Additionally, the SQL database will be running on Amazon web services, which is subject to a fee.

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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)

The costs to keep the iRISE-SOLES running after the completion of iRISE will be absorbed by the responsible partner (University of Edinburgh), which has several other SOLES and databases running and hosted on Amazon web services/shinyapps.io. The fees will be part of the partner's budget after project completion.

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Allocation of resources

Who will be responsible for data management in your project?

The task lead will be responsible for the management of the data generated by their task. The WP leads are responsible for keeping an eye on data management. The authors of this DMP (R. Heyard and G. Currie) oversee the iRISE data management and can be consulted for help if necessary.

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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)?

The iRISE consortium plans to forge partnerships with stakeholders who use the iRISE-SOLES platform to support this effort in the long term. All underlying code for the user interface will be openly available to ensure a copy can be generated at any time if an issue were to affect the continuity of the platform. For all other data output, the long-term preservation will be facilitated through the respective data repositories (OSF, Zenodo). Specifically, since Git does not have a strategy for the long-term preservation, all Git- repositories will have a working snapshot uploaded to Zenodo.

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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 outputs will be backed-up on the iRISE SharePoint to which only registered iRISE partners have access. The sensitive data will be deposited on the iRISE SharePoint and will be encrypted, with the encryption key also on the SharePoint. The access to this data, specifically to the encryption key, will be strictly controlled (e.g., only partners involved in the specific tasks will have access). For the tasks collaborating with publishers, data transfer agreements will outline the storage and access in detail. The iRISE-SOLES SQL database will be backed-up regularly and snapshots will be uploaded to OSF and made openly available.

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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).

Section 4. Ethics self-assessment of the iRISE Description of the Action discusses ethical dimensions and the compliance with ethical principles in detail. Some tasks, analyses and studies planned in iRISE will need ethical approval. This includes the qualitative and mixed-methods research planned by WP4. The strategy for ethics approval will be discussed in the respective protocols. The same is true for some of the intervention studies planned in WP5 (task 5.2, task 5.3 and 5.4). Overall, any ethics and/or legal considerations required were discussed above, e.g., in relation to sensitive data.

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Ethics

Will informed consent for data sharing and long term preservation be included in questionnaires dealing with personaldata?

Whenever personal data is collected in questionnaires or alike, participants will be informed on what types of data is collected and how and for how long the data will be preserved. For example, informed consent documentation will be created for the Delphi consultation process and focus group discussion in WP4. Further considerations will be outlined in the ethics sections of the protocols for the studies collecting personal data.

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1. Data description

Describe how data will be collected, created or reused.

Semi-structured interviews using vignette methods will be carried out with professionals, working in diverse organizational settings, such as NGOs and CSO with the aim to understand their professional practices in cases involving migrant women having a precarious legal status and experiences of gender-based violence. Interviews are carried out in different ways. Interviews can be physical meetings and recorded using a device, or else can be done digitally using zoom, and registered using the zoom option/institutional laptop. Professionals who want to take part in the study but do not want to do interviews can choose to respond to a questionnaire. The questionnaire is generated through Sunet Survey from Lund University System.

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1. Data description

What type of material (physical or digital) will you use (e.g. text, images, measurement data)? In which fileformats will you save your data?

All the interviews will be digitally recorded and saved as audio files. No name will be attached to the files, but every file corresponds to a code number assigned depending on the dates and time of the interview. The list of files will be backup in two USBs stored in a safe at Lund University, Department of Social Work. Code keys and personal data shall be kept apart and protected by a password- protected computer or server, or in a safety cabinet. Audio recordings from the interviews are also added to the PULU database. see point 4.1.

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1. Data description

According to your estimation, how large is the maximum storage capacity will you need throughout the project(primary data and revisions of processed data)?

The total amount of interviews will be up to 30 or 40.

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2. Documentation and data quality

How will your data/your material be documented and described with metadata, take collection method, content,structure, standards and formats in consideration; in order for you and other researchers or computer software toread and be able to interpret the data correctly?

The data will be transcribed into Word files who will be saved using an ordinal numeric number. All the files will be imported into MAXQDA Software and analyzed through qualitative methods, which means quotations and texts will be interpreted by the researcher and categorized in codes, and sub-codes, then grouped into themes. Text analysis could be also performed. Variables such as the size of the organizations, the main activities of the organizations, the type of human resources of the organizations, and the financial resources of the organizations in which the different respondents' work might be used during the analysis of the data. However, no references will be made to names of the cities, organization names, and any other details that could identify the workplace of the respondents. This means that also the variables described above and related to the organizations where the respondents' work will be grouped and anonymized.

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2. Documentation and data quality

How will the quality of the research data be ensured and documented (for example by repeated measurements,data entry validation etc.)?

Qualitative analysis using QDAS software ensures the reliability and validity of the analysis. Reflexivity of the researcher and repeated discussions with peers and senior colleagues will help the researcher to validate the results and findings.

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3. Storage and backup

How will you ensure integrity of storage and backup of data and metadata during the research process?

All the software I used has a license for the protection of the data. The use of Lund University systems reinforces data protection. Furthermore, all data collected and analyzed are anonymized. The files with the audio recording and the transcripts will be stored in a USB and put in a safe available in my department at Lund University. During the analysis of the data, the files generated will be saved as well in a USB and only processed through the institutional laptop.

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3. Storage and backup

How is information security and access to data controlled, for example in reference to sensitive data and personaldata?

Participation in the interviews/questionnaire is completely voluntary. All respondents have been informed about the purpose of the study, the methods used, and the data storage. All participants consent to record the data. All data are anonymized, and it is impossible to retrace any personal information protected under the GDPR. Thus, neither in the audio recording nor in the transcript is there a reference to names of participants, age, gender, race/ethnicity, and any other personal information. The access to data is also controlled together with my two supervisors. They both have access to the safe in which data are stored. We all work at Lund University, at the Department of Social Work, Faculty of Social Sciences, and work through the LU system, which ensures data protection.

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4. Legal and ethical requirements

How will you ensure that data is processed according to the regulations concerning for example personal recordhandling, confidentiality and intellectual property rights?

In this study, we follow the principle of "do no harm". In relation to respecting the dignity of participants, the researcher through a reflexive process chose carefully the methodology to be used in the study. The principle of "reciprocity" guided the process of sampling, which means the researcher gave the possibility to all the participants to use their "native language". All the material was translated and accessed in three languages, which are Swedish, English, and Italian. All participants were also informed about the methodology and were asked if they want to stay updated about the project and the results. Participants were all qualified informants, experts in their specific professional fields, and they were all discussing "the system" and their professional practices with no reference to any individual or specific identifiable situation. Participation is voluntary and all participants could drop out at any time or they were informed that at any moment they could decide to access or remove their own data from the project. Anonymity was ensured using numerical codes attached to each and every file related to interviews. Variables referring to organizational information were stored separately in an additional excel file.

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4. Legal and ethical requirements

In what way will you ensure that data is handled correctly from an ethical standpoint?

The Swedish Ethical Review Act is applied when it involves the processing of sensitive personal data as defined by the GDPR or involves the processing of sensitive personal data regarding violations of law that includes crimes, judgments in criminal cases, penal law sanctions, or administrative deprivation of liberty. Sensitive personal data (känsliga personuppgifter), or ”special categories of personal data” (GDPR), are defined as those data that reveal: • race or ethnic origin • political opinions • religious or philosophical beliefs • trade union membership • sex life or sexual orientation • health • genetic information • biometric information for the purpose of uniquely identifying a person None of the data described above is present in my dataset and no ethical review has been submitted. However, this study complies with the ethical guidelines for research, data protection, and processing detailed in The European Code of Conduct for Research Integrity (https://ec.europa.eu/ research/participants/data/ref/h2020/other/hi/h2020-ethics_code-of- conduct_en.pdf), which means that ethical principles were applied in all the steps of the research.

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5. Data sharing and long-term preservation

Will research data and/or information on data (metadata) be made publicly available?

The deliverables of my Ph.D. projects are scientific articles, conference papers, and popular scientific writings which means that metadata will be shared and discussed with the informed public such as editors and reviewers of scientific journals but only on demand. Data will be anonymized.

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5. Data sharing and long-term preservation

If so, how, when and where will data and/or metadata be made available? Are there any limitations (legal and/orethical) that prevents sharing or reuse of it?

Metadata in the form of funding and dissertation books will be sent to all the participants who showed interest and consent to access the results. A copy of the dataset containing all the material used by the researcher will be stored in a USB in a secured safe/locker at my department.

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5. Data sharing and long-term preservation

If you plan to make data/metadata publicly available, will you use a unique and persistent identifier (PID) such asa DOI?

I do not plan to use a DOI for my dataset, which means that the data recorded will not be publicly available in any platform or service.

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6. Responsibilities and resources

Who is responsible for the data management and assists with the data management during the project? Who isresponsible for data management, keeping of records and long-term preservation, after the project finished?

The principal investigator (PI) is responsible for the DMP. The two supervisors will assist the PI. Norma Montesino (supervisor) [email protected] Roberto Scaramuzzino (supervisor) [email protected] Claudia Di Matteo (PhD student) [email protected]

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6. Responsibilities and resources

What resources (cost, labor and miscellaneous costs) will be allocated to data management (including storage,backup, data sharing and long-time preservation preparation) within the project?

I have access to a safe that my department provides.

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0. Administrative questions

Name of data management support staff consulted during the preparation of this plan.

My faculty data steward, Richard Grimes, has reviewed this DMP on 06 October 2023.

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I. Data description and collection or re-use of existing data

How much data storage will you require during the project lifetime?

We expect to interview at most 20 participants. The size of each recording depends on the quality format of the recording.

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IV. Legal and ethical requirements, codes of conduct

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. If this is not the case, you can use the example below. The recordings will only be listened to by the researchers working on this project. The recoding will be transcribed, after which they will be deleted. The transcriptions will be annonymised. The datasets underlying the published papers will not be publicly released. During the active phase of research, the project leader from TU Delft will oversee the access rights to data and other outputs. The datasets will not be released publicly at the time of publication of corresponding research papers. The participation in this study is entirely voluntary, the participants have the right to withdraw at any moment during the interview, in which case we remove their data. The partipicants can withdraw their data until 31st December. After this period the data has been already processed and incorporated in scientific articles. The published materials (thesis, article) will contain statistics about the collected data and insights derived from the data.

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IV. Legal and ethical requirements, codes of conduct

Please list the categories of data subjects

Data professionals: data analysts, data engineers, data scientists outside of TUD organisation.

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IV. Legal and ethical requirements, codes of conduct

Please describe the informed consent procedure you will follow:

Signatures __________________________ _________________________ ________ Name of participant Signature Date

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VI. Data management responsibilities and resources

If you leave TU Delft (or are unavailable), who is going to be responsible for the data resulting from this project?

Asterios Katsifodimos [email protected] Assistant Professor in Web Information Systems research group. Fenia Aivaloglou [email protected] Assistant Professor in Web Information Systems research group.

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General information

Title of the project

SPR Biotichs data management plan

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General information

Short summary of the data collection

SPR Biotichs increases knowledge and operational approaches for the transition to a sustainable living environment, restoration of nature and biodiversity and sustainable use of ecosystem services.  We collect data for the data lake on organisms (terrestrial, aquatic and subterranean), environmental pressure factors, toxins, soil ecosystem services. This data is converted to a uniform spatiotemporal scale. The data is subsequently combined highlighting data gaps where data points are missing. Improving the data using machine learning techniques will fill these data gaps. Allowing for analysis of the data. Final data products are in accordance with the FAIR principles.

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General information

What is the aim of the data?

Combining this data will allow for regional assessment of effects of environmental pressure factors on biodiversity and the effect this biodiversity has on ecosystem services.

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Project plan

Reference to the project plan

see SPR Biotichs project plan

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Project plan

Location of the project plan

SPR Biotichs project folders teams

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Contributor(s) and reviewer(s) of DMP

Author(s) of the DMP

Schoffelen, N; Hofman, T; Zwaan, R; Spijker, J.

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1. Project details

Unique researcher ID:

0000-0002-1719-8573