Authors: Heather Andrews, Maria Cruz, Angus Whyte, Yasemin Turkyilmaz-van der Velden, Shalini Kurapati
To read Part 1 of the blog post follow this link.
Researchers at all levels should be equipped with skills relevant to open science and FAIR data, and the practice of these skills should be effectively rewarded and recognised. This much is clear and has been recently highlighted in the “Turning FAIR into reality” Report of the European Commission FAIR Data Expert Group. Indeed, that report states “there is an urgent need to develop skills in relation to FAIR data” and “metrics and indicators for research contributions need to be reconsidered and enriched to ensure they act as compelling incentives for Open Science and FAIR.”
To change the academic rewards system, it is necessary to define and agree upon the skills researchers need to have at different stages of their careers. This was the goal of the workshop “It’s time for open science skills to count in academic careers” held at TU Delft on 26 September 2018. This post forms the second part of the report of the the workshop. Here we present the outcomes of workshop, based on the interactive group work described below. The results of this work will be applied in EOSCpilot, which is laying the groundwork for skills development in the European Commission’s European Open Science Cloud.
Overview of the hands-on workshop
The participants were divided into four groups. Each group focused on a specific career level according to the European Commission’s framework for research careers:
- R1: First Stage Researcher (up to the point of PhD)
- R2: Recognized Researcher (PhD holders or equivalent who are not yet fully independent)
- R3: Established Researcher (researchers who have developed a level of independence.)
- R4: Researchers – Leading Researcher (researchers leading their research area or field)
The above mentioned groups were led by Yasemin Turkyilmaz, Ellen Verbakel, Maria Cruz and Alastair Dunning, respectively. The registered participants of the one day event included researchers from all career levels, librarians, data stewards, and policymakers. However R4 researchers were unavailable for the hands-on workshop.
Each group received a list of nine pre-defined open science skills together with a detailed explanation for each skill. The group activity for each of the groups was divided into two parts. In the first part, the groups were asked to shortlist a maximum of 4 skills most relevant to their respective career level (R1-R4). Subsequently, for each skill, they wrote down on post-it notes: 1. Why is this skill relevant to researchers at this career level? 2. What would be the evidence that researchers at this career level have these skills and can apply it in practice? (in other words: what does a person applying the skill do?) 3. What support (from support staff, service providers) will researchers need to apply this skill?
After displaying their ideas on whiteboards, the groups were given 5 minutes each to present a summary of their main findings to the other groups. This was followed by a short discussion followed with questions from all the groups. Below is the summary of the findings of each of the four groups.
R1 group activity summary:
The group focused on early career researchers. This group of researchers is dependant on the researchers at higher positions in the academic ladder. At the same time, often this group of researchers is perceived as being the ‘bold’ ones able to take initiatives and amenable to change. It was also recognised that R1 researchers can benefit greatly from more training and more information on open science possibilities. The four skills shortlisted by this group were:
1) Adherence to the FAIR data and code principles during and after research. The group added the term ‘code’ to this skill as well. The group reasoned that the FAIR principles represent what is necessary to have sustainable sharing and archiving of both data and code, which is important for verifiability of research. The group recognised that in order to adhere to the FAIR principles, early career researchers have to receive training as an inherent part of the curriculum. They also stressed that good supervision and getting good examples would facilitate this skill.
2) Securing funding for open science/support. The group reasoned that receiving specific training on awareness of funding opportunities for Open Science (e.g. funding for Open Access publishing) as well as preparation to secure such grants would support early career researchers to be more independent from their respective supervisors and practise open science more freely. They proposed that this could be achieved in collaboration with grant support offices and graduate schools.
3) Awareness and adherence to relevant ethical and legal policies. This skill was found to be important to overcome fear and uncertainties about ethical and legal requirements. This can make early career researchers more confident when discussing with senior colleagues the parameters these requirements set around sharing their research outputs. The group proposed Q&A catalogues which could help early career researchers better understand complex terms such as codes of conduct, legal terms, informed consent etc.
4) Recognizing and acknowledging the contribution of others. The group found it important for early career researchers to know how to properly cite data, code and methods; and how to acknowledge collaborators, technicians in the lab, etc. The group argued that if people are properly acknowledged, then they are more willing to contribute again, which is a great source of motivation for early career researchers. They expect University and Faculty policies and training to be instrumental for this skill. These should also promote the standards for using persistent identifiers, and the CRediT taxonomy for acknowledging who has contributed what to a publication.
R2 group activity summary
This group saw R2 researchers as typically those at the postdoctoral level. The group thought that postdocs were seen as researchers who are not in charge of funding nor leading projects like type 3 and type 4 researchers. Postdocs were seen as researchers focused on making effective collaborations, and working on building up their reputation. The four skills proposed were:
1) Recognising and acknowledging the contribution of others. Recognition was perceived as the main driver for postdoctoral researchers, as well as for researchers working with postdocs. The group thought that researchers need a policy framework that enforces proper recognition and receive training on how to get recognition (e.g. setting up and using ORCID).
2) Making use of open data from others. The group considered this skill as important for verifiability, and as an effective way to start collaborations with other researchers. However, to put this skill into practice, researchers need to know how to search for datasets and assess their quality. Support staff should define the standards for high quality open data, provide support in data curation and give training to researchers on best open data practices.
3) Adherence to good code management practices. This skill was also considered important for verifiability purposes and to stimulate others to reuse the code. It is seen as a quality stamp for the respective code creator, which improves the researcher’s reputation. In order to get this skill, researchers would benefit from training on version control and on writing proper code documentation. It was also suggested that researchers could learn more about these matters from research software engineers.
4) Using or developing research tools open for reuse by others. The group felt that standardisation is crucial to enable effective collaborations. Thus, researchers should receive training on the use of platforms such as github and training on metadata standards.
R3 group activity summary
This group proposed 3 skills, unlike the other groups that considered the maximum number of 4 skills (proposed by the workshop organizing committee). Largely because it was felt that the skill ‘being a role model in practicing open science’ would by definition cover most of the practical skills on the list. The group considered this to be the most important skill for R3 researchers. These researchers are already established in their careers and their fields (already developing and leading some projects), but are still very much involved in the day-to-day practice of research (e.g. they still acquire data or write software). As such, they can lead by example and influence not only earlier career researchers, particularly those they directly supervise, but also more senior colleagues. R3 researchers are very aware of the obstacles which researchers encounter when trying to change how research groups work. The proposed skills were the following:
1) Being a role model in practising open science. Stage 3 researchers were seen as researchers who are still very active in research, and but who also have a close relation with senior colleagues, the researchers in higher positions. This gives them the opportunity to establish how researchers are evaluated within their team. Stage 3 researchers have an influential role within their team, e.g. in the hiring and promotion decisions within the team. In order to do this, it was acknowledged that stage 3 researchers need support from the R4 researchers. This was a key point of discussion, because even though stage 3 researchers are seen as big influencers in the academic ladder, they still depend on the stage 4 researchers and funding committees. In relation to this skill, it was also felt that R3 researchers should not only lead by example in the way they practice open science, but should also directly influence others by speaking about it. In short, practice what they preach, and preach what they practice.
2) Securing funding for open science/support. Stage 3 researchers are involved in hiring people and applying for funding. When applying for funding for example, they should be explicit about how open science will be carried out throughout the project. When hiring, they should include open science requirements in the hiring criteria. The group also recognised that for this to happen effectively, funders need to be willing to provide funding to pay for the costs of open science activities associated with projects, and research grant offices need to advise researchers on how to include these costs in their grant applications.
3) Recognizing and acknowledging the contribution of others. The group felt this is an important area where R3 researchers can lead by example. In addition, R3 researchers are usually still building up a network and collaborations, and to do this effectively, recognition is always necessary.
R4 group activity summary
The group considered project leaders as researchers who are usually less involved in the day-to-day research practices of research. As project leaders they may be in charge of project management and involved in designing research projects, policies and regulations, vision and strategy. Nevertheless, it was acknowledged that researchers at this senior career stage still needed substantial support from their institution in order to put their vision into practice. Having this in mind, the group shortlisted the following skills:
1) Being a role model in practising open science. As project leaders, stage 4 researchers can influence a broader community (not only the researchers in their project, but also funding committees, other project leaders, executive boards, etc.). They can promote change in daily practices, but also in research policies. Stage 4 researchers could become open science role models by promoting and discussing it with their network. Advocating for open science during meetings and conferences; participating in policy development, and changing the practices within their respective groups. In order to do this, they need platforms and tools at their institutions, they also need recognition for advocating for open science, and they need support from their team members.
2) Recognising and acknowledging the contribution of others. Just like in the other groups, this group found it relevant for researchers to recognise everyone’s contribution in a project; recognising not only the scientific staff but also the support staff (laboratory technicians, data managers, etc.). In order to do it, the careers of the support staff should also be recognised for example, by creating new job profiles for data managers, data stewards, etc.
3) Developing a vision and strategy on how to integrate OS practices in the normal practice of doing research. This skill was found to help create the link between principles and actual practice. Stage 4 researchers usually work on the ‘big pictures’ of research, and thus, they have to have a vision and strategy to steer their research and project members. In doing so, they should have the advice of support staff, to ensure the feasibility of their vision. They should also have clear information about who does/can-do what within their institution, and about financial possibilities for them to turn their vision into reality.
4) Awareness and adherence to relevant ethical and legal policies. This skill was seen as relevant because senior researchers are accountable for their project team’s behaviour. Any risk of ethical and/or legal infringement will jeopardise the reputation not only of the project leader, but also of their entire group and, quite likely, the institution they belong to. Thus, it is important for stage 4 researchers to establish procedures dealing with ethical and legal issues. In order to properly do this, the institution should provide researchers with integrated support. The role of each support staff member should be well-defined (and well-informed to the researcher), there should be effective communication within the support staff, and the workflows through which the researchers can receive support need to be clearly stated.
Overall workshop summary
Overall, all groups stressed the importance of peer to peer learning: everyone can contribute to changing cultures and daily practices. All groups also agreed that proper infrastructure and policy support from institutions is required for researchers to truly implement open science practices.
Finally, recognition was seen as one of the main drivers for both scientific and non-scientific staff participating in a research project and all groups stressed the importance of proper recognition of open science practices.
The ideas and discussion generated during the workshop have given us a rich corpus of information to reflect on the workshop objectives and to envision a road map for the future to implement these ideas and discussions. The workshop outputs will be applied in EOSCpilot to help focus its Skills Framework on the key skills identified, the rationale for these, and the mapping of skills to researcher career stages together with the support requirements. Watch this space for progress and updates.
And finally a motto for everyone: change is in your hands! Everyone can contribute to change of practice in their own spheres of influence.
Authors: Shalini Kurapati, Marta Teperek, Maria Cruz, Angus Whyte
Disclaimer: In the spirit of openness and transparency, we would like to share that Shalini Kurapati wrote parts of this blog post based on the zenodo record of the presentations even though she wasn’t present during the event. Her account was verified by the remaining authors who were present.
To read Part 2 of this blog post follow this link.
Open Science is not always easy – skills are urgently needed
Open science is becoming a ubiquitous and recurring theme in the current academic environment. Researchers are increasingly expected to publicly share their research outputs (data, code, models etc.) as well as their publications. This often requires considerable effort from researchers to manage and curate their research outputs to make them shareable. But are these efforts appropriately rewarded? Emphasising the number of publications in high impact factor journals as the only valuable metric for academic promotion and hiring won’t motivate researchers to practise open science.
There is a lot of interest in changing the reward system to better align it with the actions researchers are expected to take towards more open research practices, for instance, the OSPP Rewards WG. Making sure that researchers have the right skills to do that is the other side of that coin. To change the rewards system, we have to understand and identify the skills researchers should be rewarded, and recognise that these may change at different stages of their academic careers. This was precisely the goal of the workshop on 26 September 2018 that we organised at TU Delft jointly with the EOSCpilot. The EOSCpilot project is laying the groundwork for the European Open Science Cloud, and wants to offer a framework for institutions and others to develop the skills needed for researchers, data stewards, and others who support research to help put open science into practice.
The workshop was aptly titled “It’s time for open science skills to count in academic careers” (#openskills18). The workshop format combined presentations on related topics with interactive group work in the afternoon. In this post, we summarise the presentations and in a separate blog post we’ll present the outcomes of the workshop and will reflect on the key findings /thoughts on future steps.
The aim and format of the workshop was presented by Valentino Cavalli of LIBER and EOSCPilot. In his welcome note, Mr.Cavalli explained barriers to open science in the European and wider academic context. These barriers include a culture of disincentives, fragmentation between infrastructures, interoperability issues and access to computational resources. He highlighted that the workshop would focus on the culture of disincentives, which has to be changed such that researchers at all careers levels are equipped with relevant skills and suitably rewarded for putting them into practice
The opening talks were delivered by Ms. Anne de Vries (PhD students Network Netherlands), Prof. Bartel Van de Walle (TU Delft) and Mr. Rinze Benedictus (UMC Utrecht).
Ms. Anne de Vries shared the perspectives of the eurodoc, the European council of doctoral candidates and junior researchers on open science policy and practices. She stated that it is important to identify and train open science skills for early career researchers based on their disciplines. Early career researchers should also be made aware on how to make their outputs FAIR and how open science skills will not only take science forward but also positively influence their careers. She also reflected that senior staff should support early career researchers in practising open science and thus also need appropriate education and training.
Prof. Bartel van de Walle spoke on the open science policies and practical examples from his domain of information management during humanitarian crisis response. He also presented the challenges of implementing open science due to the inertia in research institutions that are often resistant to change. He insisted that open science is not just a requirement of funding agencies but is the right way forward to democratise science and achieve the UN’s sustainable development goals. He also pointed out the waves of change and indicated an example of successful implementation of open science policies in practices like McGill University’s Neurological institute and hospital. He concluded his speech by saying that open science is just science done right.
Mr. Rinze Benedictus delivered a powerful message with his talk: institutions should not equate the impact of a research work to an impact factor of a journal. He displayed the reinforcing loop of how authorship in high impact journals is an incentive for researchers for receiving more funding and recognition and to continue with the cycle of publishing to increase citation scores. He showed the damning evidence from The Lancet and Nature about the reproducibility crisis in science due to the earlier said focus on publishing to establish scholarship. While referring to global initiatives such as the DORA to change attitudes among institutions and individual researchers, he gave a concrete example of how UMC Utrecht is implementing good practices in rewarding researchers. For instance, at evaluation meetings at UMC, a researcher would be asked “How did you arrive at your research question and what are your next steps”? Rather than the traditional “what is your measurable output”.
Dr. Simon Kerridge (CASRAI) gave a talk on the CreDIT taxonomy. The problem that CreDIT tries to solve is that the current authorship criteria in publication doesn’t give sufficient recognition for various contributions of researchers. In addition, authorship credit alone doesn’t support accountability for the research results. He stated that since science is increasingly a team effort, credit needs to be given where due to incentivise researchers for their unique contribution. He explained that the CreDiT taxonomy aims to offer a role based credit systems, where the contributors can assign themselves credit for 14 tasks: writing, supervision, review, data analysis, project management and so on. Finally, he presented the vision for the future of increasing the awareness of the CreDiT taxonomy and to create feedback mechanism to evaluate future versions and to link it platforms like ORCID and Crossref.
The closing remarks of the workshop were provided by Ms. Anette Björnsson (European Commission) and by Mr. Kevin Ashley (Digital Curation Center & EOSCPilot).
Ms. Anette Björnsson reflected on the current initiatives within the European Commission towards changing academic rewards. She highlighted the importance of several recent reports produced by EC Working Groups: Evaluation of Research Careers fully acknowledging Open Science Practices, the report on Next Generation Metrics and Turning FAIR Data into reality. All of them influence the current thinking at the European Commission and also help shape the mission and vision of the European Open Science Cloud. She also stressed that large collaborative efforts at the European level require cooperation and consensus between all EU Member States, which often require time and patience. The situation is no different when it comes to the implementation of policies and changing practices in open science: individual Member States are at different stages of implementation and have varying levels of infrastructure and personnel currently available to them. However, Ms.Björnsson ensured us that while sometimes slower than desired, change is coming. Given that EOSC is a collaborative, pan-European endeavour, the chances are that changes brought with the EOSC will also be more effective and sustainable long-term.
Mr. Kevin Ashley then continued reflecting on the discussions which took place throughout the day, and in particular, the points raised by researchers during the interactive workshop part. He stressed that the common priority to all researchers, regardless of their career stages seems to be to get the recognition they deserve for Open Science activities. He reflected that there are (numerous) barriers to practical implementation of Open Science and to rewarding those practising Open Science appropriately, but that these barriers should not stop anyone from changing the status quo. As Dr. Maria Cruz beautifully summarised in her tweet, based on Mr. Ashley’s words: It’s possible to change the academic rewards system. It’s possible for PhD students. It’s possible for senior researchers. And it’s possible for institutions.
The format, content and outcomes of the hands-on workshop during the event, together with some reflections and thoughts on next steps are published in a separate blog post.
- All presentations of the speakers can be viewed and downloaded here.
This blog post was written and originally published by Loek Brinkman on his own blog.
On the 26th of September, I participated in the event “Time for open science skills to count in academic careers!”, organised by the European Open Science Cloud Pilot (EOSCPilot) and the 4TU.Centre for Research Data. The goal was to define open science skills that we thought should be endorsed (more) in academic career advancement.
The setting was nice: we were divided in four groups, representing different stages of academic careers (from PhD to full professor) and discussed which open science skills are essential for each career stage. What I liked about the event was that the outcomes of the discussion were communicated to representatives of EOSCpilot and the European Commission. So I’m optimistic that some of the recommendations will, in time, affect European research policies regarding career advancement.
On the other hand, I think we might be skipping a step here. Open science is often talked about as a good thing that we should all strive for (in line with the (in)famous sticker present on many laptops of open science advocates: “Open Science: just science done right”), as though open science is a goal on itself. To me, this doesn’t make a lot of sense. There is no clear definition of open science. It is an umbrella term covering many aspects, e.g. open access, open data, open code, citizen science and many more. So, in practice, people use various definitions of open science that in- or exclude some of the aforementioned aspects of open science, and differ in how these aspect should be prioritised. That means that while many people are in favour of open science, they may disagree greatly on what they think should be addressed first and how.
I don’t see open science as a goal. I see open science as a means to achieve a goal. I think, we should first agree on the goal: specify what we want to change or improve. The way I see it, the goal is to make science more efficient – to achieve more, faster. Starting from this goal, several sub-goal can be defined, such as:
(1) making science more accessible,
(2) making science more transparent & robust,
(3) making science more inclusive.
Open science can be a means to achieve these subgoals. Depending on how you prioritise the subgoals, you might be more interested in (1) open access, (2) open data and code, or (3) citizen science, respectively.
It is not too difficult to come up with a list of open science skills for academics, and it would be awesome if those skills would be endorsed more in academic career advancement. But we first need to define the goals we want to achieve, before we can start to prioritise the means by which these can be achieved. If the endorsement of open science skills can be aligned with the overall goals, then we are well on our way to make science more efficient.
Authors: Wilma van Wezenbeek, Alastair Dunning, Marta Teperek
Date: 3 August 2018
- Report: Prompting EOSC in Practice (Rules of Participation are on page 28): https://www.eudat.eu/sites/default/files/prompting_an_eosc_in_practice_eosc_hleg_interim_report.pdf
- EOSC Open Consultation website: https://eoscpilot.eu/open-consultation
Structure of our comments
- Overarching comments
- Specific comments on the Report Prompting EOSC in Practice
- Comments on specific Rules of Participation
- Overall, the report is a useful outline of the vision for the European Open Science Cloud (EOSC), with concrete proposals for the rules of participation.
- Recommendations for working with commercial partners need to be carefully thought through. In particular, mechanisms need to be in place to ensure that commercial partners participating in the EOSC do so on the same rights as non-commercial partners.
- Care needs to be taken when deciding on recommendations for Member States and for research communities. The latter are always international in nature.
- It is not clear why additional intermediaries are required for managing financial contributions to the EOSC partners. Couldn’t transactions be arranged without additional intermediaries, which increase the costs and complexity of the ecosystem?
- At some places the recommendations are unclear, lacking structure and alignment. It feels as if the founding vision for the EOSC is not clear enough to create harmonised rules for participation.
- The chapters on business model and financing feel like “far away” from today’s practice.
Specific comments on the Report Prompting EOSC in Practice:
Page 8 and 9 (Executive Summary)
“To help drive forward and implement the EOSC, the main thread of the report is to understand how the EOSC can effectively interlink People, Data, Services and Training, Publications, Projects and Organisations.”
- Should “labs and instruments” and “places” be added there as well?
“The EOSC should implement “whatever works” and do “whatever it takes” to increase the availability and volume of quality & user-friendly scientific information on-line.”
- Why should EOSC focus on “ volume”? This is an odd phrase to use.
“Define an EOSC Quality of Service (QoS) standards, separate for all elements of the ecosystem (data, data access services, software, etc.), to develop a trustable ecosystem.”
- Publications should also be part of the EOSC ecosystem
“Introduce, as part of EOSC’s mission, that a state of the art analysis is carried out on a national level within the Member States for assessing statistics and key assets around the composition and relevant clustering of the community of users, with the respective eInfrastructures & research infrastructures & scientific communities.”
- Why do this on a national (Member State) level? That is not the way these communities are constructed, or how science works.
“The universal entry point to the EOSC should provide access to a marketplace of efficient and effective services, with lightweight integration (authentication and authorization infrastructure, order management, etc…) where service providers can find service users and vice versa. Nothing is wrong with a number of multiple entry points which should be seen as a plus rather than a negative fragmentation.”
- This recommendation is unclear. We want a universal entry point, but promote decentralised ones?
“Introduce a regular assessment of EOSC against other alternatives, including commercial providers. This could be made to either enhance an EOSC Service, or to support new Services;”
- Alternatives to EOSC? This recommendation is unclear.
“Build a workforce able to execute the vision of the EOSC by ensuring data stewards, data and infrastructure technologists and scientific data experts who are trained and supported adequately.”
- I notice here and also in the six action lines on page 14 an expansion of what the EOSC should be or become, and envisions. The question whether that actually will help pace and clarity. Is EOSC not slowly taking over all the topics laid down in the OSPP?
“All activities mentioned above have a stronger focus on research data as opposed to services for research data management.” (page 17)
- Why would “research data” and “research data management” be presented as opposing activities?
“Flexible ways to access and share data and direct access to fast networks to do so are at the top of the agenda for researchers.” (3.2, page 19)
- This is not true at all. What about inability to find datasets because of lack of interoperability and integrated resources? Or the lack of recognition for good data management? Or not being rewarded for doing thorough and reproducible research?
3.4. Governance (page 21)
“Cooperation is needed between end user, service providers, and funding agencies / policy makers.”
- And what about the organisations that represent the end users?
- How the depicted layers and other existing temporary or structural governing bodies will work together (e.g. OSPP, Science Europe, ALLEA, EUA, etc.)
4.2 Business model (page 23)
“The EOSC Business model is a critical non-technical element that will determine the success of the EOSC vision.”
- Scoping principles around the business model requirements are also needed, to outline governance structure (of the infrastructure or service itself), community involvement, sustainability (see the paper by Bilder and Neylon), ownership and openness
“The currents model for provisioning access to Research Infrastructures is based on the guidelines contained in the Charter for Access, where three main models are described” and “a model based on the Wide Access mode modulated by a negotiated, agreeable Access restriction, is the pragmatic way to start moving with the EOSC. Private providers willing to provide resources within the EOSC framework will envision a Market-Driven approach to support users.”
- Also in reference to the guiding principles, along with the business model, it seems good to set Wide Access as the default, and jointly decide where exceptions are allowed. Simply saying that private providers will envision a Market-Driven approach seems to be against the Rule of Participation 5.1. that “Private sector users should be considered stakeholders in the EOSC as well as participants from the start, not added after (…). By participating, private sector may want to invest in the long-term development and sustainability of the EOSC, along with the public sector and not just serve to exploit public data for free.”
- Also, while Excellence-Driven Access model and Market-Driven Access are well defined, the principles behind the Wide Access model need to be better articulated.
“To coordinate acquirement, the EOSC and member states would also certify one or more brokers to manage the acquisition, distribution and payment for EOSC vouchers. These brokers could be government agencies in member states, entities within member states, transnational governments or private firms” (p.25)
- Why would it be necessary to involve brokers in the process?
4.3 Funding Model and Payment Mechanisms (page 25)
“…similarly to how YouTube pays people who upload videos based on how many times they are viewed.”
- You need registration of your account and be compliant to get paid by YouTube, so that is not the default situation. We would plea for a null or onset situation based on reciprocity, not immediately starting with payments.
- Difficult to judge what would be the best fit. Also, guiding principles are needed here, e.g. transparency, efficiency and simplicity. Is there a way to avoid the giant profit margins being made by some players in the scientific publications industry? What principle should be used to achieve this? What have we learned from the big deals? We want researchers (end users) to be cost-aware, without stressing them with workflow troubles and micropayments.
- In Direct Support: the disadvantage “Resources can have internal foci, reducing access from outside stakeholders” could be easily overcome by establishing clear funding rules demanding equal access rights to internal and external stakeholders
- In Direct Support: the disadvantage “Burdensome for commercial entities, even where they could provide significant cost savings and be incentivized to innovate.” – this is unclear to me – why burdensome, and why specifically for commercial entities?
Comments on specific Rules of Participation (from p. 28 of the report Prompting EOSC in Practice )
5.1 Federating the existing infrastructures
“Private sector users should be considered stakeholders in the EOSC as well as participants from the start, not added after (…). By participating, private sector may want to invest in the long-term development and sustainability of the EOSC, along with the public sector and not just serve to exploit public data for free.
Brokers would be obliged to behave in a disinterested fashion with all providers. Entities that establish brokers must require that the broker does not establish a monopoly, or fall under the control of a service provider that then uses their influence to exclude other service providers from the marketplace.”
- How is this going to be achieved in practice?
5.2 Eligibility criteria for actors
“Key rules for participants therefore will include”
- These rules for actors are also interlinked with the eligibility criteria for data and for service providers. Perhaps it would be valuable to map them.
- Identifiers and Metadata:
“While maintenance of this metadata is fundamentally the responsibility of the submitter of data or other digital objects…”
- Why would maintenance of metadata be the responsibility of the submitter of data and not of the data service provider/repository?
5.3 Participation according to the business model
“The development of novel capabilities, long-term storage/maintenance of data resources and fixed cost capabilities are likely to be provided using direct payments to organisations setting up nodes in the EOSC. By contrast, numerous research activities by individual investigators may be supported via EOSC vouchers. Nodes in the EOSC will have to be able to engage with the business model. This will probably imply a business arrangement with the brokers set up by funding agencies in order to accept these vouchers as payment.”
- Why need brokers? Couldn’t transactions be arranged without additional intermediaries, which increase the costs and complexity of the ecosystem?
“As the submitters control access, they retain liability for data leakage and to ensure that relevant individuals accessing information meet the necessary requirements.”
- Why would submitters, and not the service providers, be responsible for access control and be liable for data leakage?
“As regards to data quality and warranties as to fitness for purpose, the EOSC MVE would need to operate under the principle of caveat emptor. That is, while submitters may be liable for outright fraudulent data, the nature of scientific research data determines that EOSC data should probably be provided with no warranties for any particular purpose, although Section 5.5 section below, on assessing data quality, should be also taken into consideration.”
- What does “the nature of scientific research data determines that EOSC data should probably be provided with no warranties for any particular purpose” mean? Is it not contradictory with the statements which follow straight after that: “Data should be: »» processed lawfully, fairly and in a transparent manner in relation to the data subject (principle of ‘lawfulness, fairness and transparency’); »» collected for specified, explicit and legitimate purposes;”
- The GDPR is about data “processing”, not about data “collection” only. Data re-use is also form of data processing. The statements above seem contradictory to “no warranties for any particular purpose”
5.5 Data quality
- Suggestions are made that search results for datasets could be arranged based on reviews, views etc., and a comparison is made to TripAdvisor. I find this rather worrying: 1. Isn’t there a risk that this would lead to a high risk of data/score manipulation? 2. Wouldn’t it lead to self-perpetuation of certain objects/datasets? (similarly to what happened with journal impact factors)? 3. This could also be very detrimental to certain disciplines