Final Report Summary - SIM4RDM (Support Infrastructure Models for Research Data Management) Executive Summary:SIM4RDM stands for Support Infrastructure Models for Research Data Management. SIM4RDM is a 24-month European project funded through the European Seventh Framework Programme under the theme ‘Cooperation for ERA-NET supporting research structures in all Science and Technology fields’. The project team is made up of six partners from different parts of Europe: Jisc (United Kingdom), HEA (Ireland), NIIF (Hungary), Nordforsk (Norway), CSC (Finland) and SURF (Netherlands). The aim of SIM4RDM is to enable researchers to effectively utilise emerging data infrastructures by ensuring that they have the knowledge, skills and support infrastructures necessary to adopt good research data management methodologies. Good data management is essential for both productive research and optimal use of the new data infrastructures. Effective management of research data is crucial for generating economic and scientific progress and for preserving this capital for future generations.The initial activity of SIM4RDM were a landscape study of current research data management practice and aspirations, from which some key recommendations as well as ideal research data management practice for different actor in the research data management landscape: Funders, Infrastructure Providers, Institutions (such as Universities or other Research Institutes), Publishers and Researchers. From this and a literature review, SIM4RDM has produced a "cookbook" of potential intervention actions to improve data management practice which can be taken by these various actors Each actor can assess their current data management practice maturity using an assessment framework; the cookbook then recommends actions which can be undertaken to improve research data management practice. The cookbook also indicated related joint actions which can be undertaken by other actors. Whilst the cookbook is primarily paper based, a demonstrator of an online interactive tools has also been developed. The “cookbook” was tested in a number of case studies which had immediate impact on the communities involved.Project Context and Objectives:The vision of SIM4RDM is to enable researchers to utilise effectively the emerging data infrastructures by ensuring that they have the knowledge, skills and support infrastructures necessary to adopt good research data management methodologies. SIM4RDM will achieve this by analysing existing funding programme interventions within member states and internationally, and producing models, evaluation frameworks and policy recommendations for future national interventions and pan-European and international co-ordination. Good research data management practice is essential for productive research and for taking full advantage of the emergent data infrastructures. Whilst there are established practices in the management and referencing of publications, similar standards for the management of data are sporadic, with some disciplines better organised than others. Typically publications do not include the underlying data upon which their conclusions are based and this data is not accessible. Effective management of research data is critical for generating economic and scientific advances as well as for the preservation of this capital for future generations. To date, most data infrastructure activities at national and international levels tend to focus on the technical developments needed for both large and small scale data infrastructures, or on technical standards for data management and data referencing. Within the UK, JISC has run a national funding programme for research data management explicitly aimed at developing the social infrastructures needed to help build skills and capacity amongst researchers to take advantage of these technical initiatives. As the Riding the Wave report has indicated, JISC is not alone in assessing the support and the policy and cultural changes needed by the emerging data infrastructure. Similar issues have been recently discussed at an international level, for instance at a US National Science Foundation workshop entitled ‘Changing the Conduct of Science in the Information Age’. In Australia, good data management practices have become a requirement for researchers and institutions under the Australian Code for Responsible Conduct of Research, and with the Australian National Data Service an internationally highly respected organisation exists that provides funding and expertise to help researchers in this area. The purpose of the SIM4RDM project will be to co-ordinate actions across the EU, and ideally involve other international stakeholders to:i) Share knowledge and experience in building the support infrastructures and encouraging research data management practice in the form of models for intervention programmes and evaluation frameworks. This will be undertaken throughout the project via the website, expert workshops, conference presentations, online collaboration and publication of newsletters.ii) Determine current national and pan-European research data management practices and types of interventions implemented by programme owners and managers. This will be accomplished by undertaking an online survey with results published in a landscape study.iii) Build a funding framework model to facilitate co-ordination of such interventions across Europe and ensure maximum impact. iv) Build an evaluation framework for measuring the maturity of these interventions, and ways to improve it. v) Implement and pilot the funding and evaluation frameworks, and produce recommendations for their future improvement. vi) Work towards building international consensus on long term research data management strategy and policy. Project Results:Evidence Gathering ------------------The project started with an assessment of the European research data management (RDM) landscape. The main aims of the Evidence Gathering work package were: 1) to survey the existing and planned interventions for improving research data management skills, support and capacity in a sample of European countries; 2) to produce a landscape study of the research data management awareness and practices, with recommendations for national and European policy makers responsible for research data.Based on the target areas identified by the Jisc Managing Research Data Programme model, complemented with further desk research, an online survey was conducted within a sample of RDM stakeholders across Europe. The survey aimed to establish what RDM interventions and practices are being used by funding agencies, research institutions, national bodies and publishers across European member states to improve the capacity and skills of researchers in making effective use of research data infrastructures. Based on the survey results, a landscape report of the European RDM practices was produced, which included recommendations for each stakeholder group aimed at improving the effectiveness and coordination of RDM interventions.107 research institutions, 20 research funders, 18 national research infrastructure providers, and 7 publishers participated in the survey. The main findings of the study were presented at an expert workshop attended by participants from all main stakeholder groups. The survey results indicated that the European RDM landscape is extremely complex and therefore difficult to describe in general terms. Overall the number of research institutions with a data management policy is growing. Of those institutions without a policy, 42% intend to define one within the next year. The main driver for adopting a policy is funder requirements. For institutions without a policy, academic demand or general commitment to open access can drive the establishment of a data management plan. 15% of the research institutions that have responded to the survey actually prescribe a data management plan. 50% of the responding organisations offer an infrastructure for storage and management of and access to research data made up by a variety of file storage and library systems. Research institutions should develop policies that contained elements that support scholars and scientists in their management.Interviews with researchers indicated that the main driver for writing a data management plan is the requirement of funder or publisher. Nearly half of the research funders who took part in the survey indicated that they have a policy covering research data management, whilst a quarter of them require that data management plans are part of the grant application. Data management plans tend to cover data acquisition, use and re-use, storage and protection, and rights of ownership. Just over one third of respondents indicated that they recommend a specific organisation for data preservation.Funding bodies were recommended to encourage researchers by offering clear instructions on building data management plans at project proposal stage. More funding calls should be issued specifically for research data management. Funders could also help researchers by suggesting specific trusted repositories for sustainably storing their research data.Across member states various types of national research infrastructure providers have been established to coordinate research data management activities. Most of these national bodies are funded by the government, usually at project level. Their main remit tends to be the long term preservation and sharing of research data, assisting funders with the elaboration of research data policies, and building RDM infrastructure. The report recommended that national research infrastructure providers take the lead in drafting national codes of conduct that encourage the creation and use of data management plans. They should also suggest and/or supply appropriate tools and adapt these to the national context. Furthermore they should take an active role in data citation practices. For publishers, policies have yet to become established instruments. Policies that do exist, require readers and reviewers to provide links to the data underlying the article. In some cases publishers also require them to make entire datasets available when submitting the article, but not to keep them up to date. Publishers generally do not employ standards for data. Digital object identifiers are indeed used for citing, but data are cited in different ways. A dialogue should be established with publishers and publishers’ associations about the definition of data policies. Possible elements are persistent identifiers for citation of data and requirements of reliability for repositories in which data are to be deposited.Interviews with researchers indicated that research data policies should primarily cover roles and responsibilities for managing data, mechanisms for storage, backup, registration, deposit and retention of research data, access to re-use of data, open accessibility and availability of data and long term preservation and curation. They should also indicate available data infrastructures and sustainable workflows for data publishing and archiving. In addition, such policies should include arrangements for crediting researchers that made available their research data along with research publications. Researchers indicated they would significantly benefit from face-to-face support and training, but they indicated that flyers, meetings, seminars and presentations are not very effective. The setting for research data management is broader than initially anticipated. Other stakeholders need to be brought in as well, e.g. editorial boards of scientific journals, data centres and infrastructure providers. Research societies may intervene with the development of common practices. Infrastructure providers could intervene with common data formats for preservation and storage, tools and utilities. Policies from funders, institutes and editorial boards may influence researchers to use the principle of ‘share and share alike’.The report was circulated via the mailing list and newsletter, as well as individually to relevant bodies (including Eudat, RDA, e-IRG), and feedback was received and incorporated. Presentations of the main findings were made, and paper leaflets were distributed at several well attended workshops in Berlin, Rome, and Dublin. The updated landscape report included tables with the geographic distribution of survey respondents, and an enhanced description of survey data.Research Data Management Intervention Framework-----------------------------------------------The key objective of this work package was to build an intervention model that can be used by research funders and programme owners to design and run funding programmes that could achieve measurable improvement in the provision of research data management. Landscape study findings showed that the European RDM landscape is complex, with huge variation of maturity levels and stakeholders tending to work in isolation from each other. The project's initial view of the RDM landscape focused on researchers, their institutions, research funders and national infrastructure providers. It gradually became clear that the overall picture is more complex, with research publishers, independent infrastructure providers, non-governmental funders and professional associations also playing a part. To address this complex landscape, a so-called “core-plugin” model was devised. The “core” element focused on a basic set of RDM issues likely to be encountered across Europe, while the ”plug-ins” covered the medium and more advanced RDM issues that tend to vary across countries and stakeholder groups. This model built on previous work carried out by various organizations and projects, including Jisc, UKOLN, DCC, ANDS and others, to which it added its own cross-cultural and cross-stakeholder dimensions, in line with the project's vision and the findings of the Landscape study. The original aim of helping funders better design and evaluate RDM interventions was thus expanded to include opportunities for other stakeholders to increase the effectiveness of RDM policies and to improve RDM awareness and practice across Europe and beyond. The model extended previous work by including actions for five different stakeholder groups: Funders, Infrastructure Providers, Institutions (such as Universities or other Research Institutes), Publishers and Researchers. Five RDM areas have been identified as critical: organizational, legal, technical, user experience, and data reuse. A number of frequently mentioned barriers and enablers were analysed, and “target lists” highlighting the features of an ideal, fully mature RDM landscape were devised. From these target lists, a series of step-by-step actions for each stakeholder group were drawn, with the aim of helping them move towards higher RDM maturity including possible joint actions with other actors.These series of step-by-step activities were grouped together in the so-called “SIM4RDM cookbook”. Each cookbook entry was labelled according to its intended stakeholder, RDM area, and maturity level, to allow for consistent cross-referencing with the Evaluation Framework. The cookbook was subject to a number of iterations based on feedback collected during project workshops and case studies.A typical process for increasing RDM maturity involves a number of steps, as detailed below:1. In a first phase users self-assess their initial RDM maturity using an evaluation tool that can be tailored according to their needs.2. The assessment results can be visualised on a multi-axis diagram to help users prioritise the RDM areas they want to improve. 3. The self-assessment results automatically provide sets of cookbook entries with step-by-step activities aimed to increase RDM maturity. 4. To help users implement these activities, best practice information is available in a knowledge base, and users' previous experience is showcased in case studies collected from across Europe. 5. Having decided on the RDM aspects they want to improve, users complete the suggested activities, re-assess themselves and compare their new levels with the initial ones. The cycle can be reiterated to reach higher maturity. 6. User feedback on the effectiveness of the framework is gathered, and new case studies are added to the knowledge base, thus improving the framework's performance and boosting its role in RDM collaboration.A number of workshops to validate and refine the work on the Funding and Evaluation Frameworks were organized in conjunction with several key research data management events: • Research Data Management Landscape Workshop - Zandvoort, Netherlands, 25-26/4/2012• “Hands-on Research Data Management” – Berlin, 12/04/2013 (collocated with the Knowledge Exchange “Making Data Count” workshop)• Cross-stakeholder research data management – Rome, 28/10/2013 (collocated with Eudat project's 2nd Conference) • Building collaboration for research data management – Dublin, 25/03/2014 (collocated with Research Data Alliance 3rd Plenary meeting) The frameworks were regularly updated by incorporating online feedback and comments from presentations and other dissemination activities. Some suggestions for improvement included:• Adapt terminology to each stakeholder group• For highly specialised RDM issues include more basic level activities• Design activities with a demand-driven service in mind• Use examples/ scenarios to clarify the more advanced activities• Provide a facility to skip irrelevant questions• Provide relative metrics (benchmark) to help users see where they stand in relation to othersEvaluation Framework--------------------The purpose of the Evaluation Framework was to build an effective and flexible model for assessing the RDM maturity of funders and other stakeholders across Europe using a simple, easy to follow process. Work started with identifying the main motivations and driving forces animating the stakeholder groups surveyed in the landscape study. While closely related, these groups turned out to be significantly different from each other, with different approaches to, and expectations from, RDM. A common frame of reference to assess the RDM maturity of all players was necessary, but flexible enough to address user specific needs. An initial model highlighting the relationships within and beyond the RDM ecosystem was produced. Then, with input from the Landscape study and Funding Framework, an Evaluation Framework with the following main features was devised:• Ecosystem-oriented: designed to address both common and specific needs of main stakeholder groups• Dynamic: aimed for users at various maturity levels and RDM implementation stages;• User-customisable: allows new assessment criteria and investigation areas to be added without affecting overall evaluation process;• Benchmarked: provides both absolute and relative results, to allow positioning against previous users;• Hybrid: allows for a combination of qualitative and quantitative assessment;• Modular: structured on five RDM areas (organizational, technical, legal, user engagement, data reuse);• Hierarchical: structured on 3 maturity levels (baseline, intermediate, advanced); • Relational: consistent referencing across questionnaire and cookbook to allow each question to be linked with a cookbook activity• Cross-correlated: allows identifying trends and commonalities within and across stakeholder groups.In line with the Funding Framework, five questionnaires were created, one for each stakeholder group (funders, infrastructure providers, institutions, publishers, researchers). The questionnaires are structured in sections addressing five main RDM areas: organizational, technical, legal, user engagement, and data reuse. Each question provides six answers to choose from (2 baseline, 2 intermediate, 2 advanced). Upon selecting the most appropriate answer for each question, users are presented at the end of the questionnaire with an overall score indicating their RDM maturity level. The assessment results are cross-referenced with the Funding Framework cookbook, which provides specific recommendations to improve users' RDM practice. As with the Funding Framework, the Evaluation Framework questionnaires were updated with feedback from project workshops and case studies.Case Studies------------The aim of the Case Studies work package was to test and improve the SIM4RDM Funding and Evaluation frameworks. The work was carried out with help from stakeholders in four European countries, who self-assessed their RDM maturity and provided feedback for future improvement.The institutions involved in the four case studies are presented below.1. Lorraine Earth and Environment Observatory (OTELo) is a group of four research units from French research institutions who have established a partnership with a scientific and technical information support unit, INIST-CNRS. Together with promoting RDM at local and national levels, the main aim of this case study was to use the SIM4RDM frameworks to explore how social infrastructure (INIST) can help institutions and researchers coordinate their actions to improve RDM maturity and practice.2. The Department of Communication Science at VU Amsterdam plans to develop an openly accessible online platform for social science research to facilitate research workflows within the scientific field. The platform aims to increase transparency and reproducibility of research and contribute to cumulative knowledge building. The success of the platform depends on the critical mass of future users; therefore it is extremely important to identify the features most likely to boost user engagement. The case study used the SIM4RDM frameworks to identify critical factors for user engagement, hence making the future platform more effective. 3. Nordic Center of Excellence Justice in Education (JustEd) is a joint initiative of the five Nordic countries aiming to address the following question: “How do systems, cultures and actors in education facilitate and constrain justice in the context of the globalising Nordic welfare states?”. Basic data management development relevant to JustEd has taken place in Finland, and is now rolled out to the other groups. The case study involved researchers within social sciences who mostly work with sensitive, qualitative data.4. The Library and Information Centre of the Hungarian Academy of Sciences (OTKA) maintains a National Scientific Bibliography Database that tracks Hungarian research publications. The Hungarian Academy of Sciences has recently adopted an Open Access (OA) policy concerning research publications. The Library and Information Centre plans to implement a software tool to expand the capability of this database, provide statistics on OA compliance, and increase the OA awareness at local and national levels. The case study focused on assessing the relevance of SIM4RDM frameworks for improving the technical capability of the software used to manage OA research metadata. Overall, the four case studies indicated that the SIM4RDM Funding and Evaluation Frameworks were extremely useful in the context of their institutions' efforts to move towards more effective, better coordinated research data management. At the same time, the feedback collected from the case studies was essential for testing and refining the frameworks so that they better address user needs. The case study reports outlining the main findings are available at http://sim4rdm.eu/documents/project-outputsA combination of high level and low level recommendations were produced as part of the case studies:High level recommendations:1. Build better awareness of promotion of information and good practice at all levels: institution, research communities, national and international2. Run basic "101 courses", ideally provided by institutions to all levels, including at undergraduate and graduate levels3. Adapt the highly technical vocabularies used by specialists to the understanding of those unfamiliar with RDM needs and requirements.5. Develop available easy to use RDM assessment tools (like ours). These may need to be tailored to different communities.6. Encourage institutions to adopt data policies at institutional level7. There is a need for a registry of research data management policies at all levels (institutional, national and international; institutions, funders, publishers, etc.), and perhaps a common vocabulary for such policies.8. There is a need as a possible extension of such a registry for the means to monitor the take-up, compliance and impact of such policies.9. There is still a need to identify incentives for researchers use good RDM practice, to make both the economic and scientific case for research data management.10 More support is needed for research data management at the institutional level - providing the social infrastructure for research data management.11. Research data management solutions need to be flexible and easy to adapt to various research disciplines and data12. Align legal requirements for data sharing across regions13. Acknowledge and address the different research data priorities across the EULow level recommendations:1. Provide a glossary of terms related to research data management.2. Adopt vocabularies and terminology adapted to different categories of respondents (cf. basic, intermediate, advanced levels). 3. Simplify the content of the questionnaire and cookbook to ensure their comprehensibility. 4. Agree on further actions regarding RDM knowledge help desk.5. Refine the wording of questions and answers in the SIM4RDM framework6. Check correlations between questions and answer, and gradation of answers to reflect increase of RDM maturity in the SIM4RDM framework 7. Improve the logical structure of questionnaire and cookbook 8. Improve SIM4RDM framework capacity to adapt to specific research areas and disciplines9. Provide a version of the SIM4RDM questionnaire to assess generic RDM issues relevant to a variety of scientific domains.10. Link the SIM4RDM assessment tool to a database of available references and use cases to help with the implementation of suggested actions 11. Build monitoring and feedback capabilities as premium features on top of the basic functionality 12. Develop an overall process for communicating goals, task implementation, outcomes and benefits to encourage users to regularly evaluate their RDM maturityProject management------------------The work package leaders established at the beginning of the project met on a regular basis via online conferencing. Initially this was on a fortnightly basis, however in order to improve momentum the online meetings were re-scheduled on a weekly basis, alternating between all partners and those involved in the currently active work packages.A number of face-to-face meetings, scheduled around key project dates, were hosted in turn by project partners, as follows:JISC: Birmingham, UK, 26 Jan 2012SURF: Utrecht, Netherlands, 8 May 2012NIIF: Budapest, Hungary, 2 Sep 2012NordForsk: Oslo, Norway, 10 Jan 2013CSC: Helsinki, Finland, 7 Jun 2013Jisc: London, UK, 14 Nov 2013Notes and actions summing up discussions and decisions made were circulated regularly on the project mailing list. An Advisory Board was established at the beginning of the project with the following members: • John Wood, Association of Commonwealth Universities, United Kingdom (chair) • Jun Adachi, National Institute of Informatics, Japan • Pam Bjornson, National Research Council, Canada • David Bohmert, European Strategy Forum on Research Infrastructures, Switzerland • Jan Brase, DataCite, German National Library of Science and Technology, Germany • Rachel Bruce, Joint Information Systems Committee, United Kingdom• Amanda Crowfoot, Science Europe , Belgium• Marc Dupuis, SURF, Netherlands • Balasz Konya, Lund University, Sweden • Leif Laaksonen, IT Centre for Science, Finland • Pat O'Connor, Higher Education Authority, Ireland • Andrew Treloar, Australian National Data Service, Australia • Susan Winter, National Science Foundation, United States The initial board changed during the project as some members changed jobs or responsibilities. In most cases they suggested replacements or delegated colleagues to attend meetings in their place. Whenever possible the project board meetings were collocated with international events to increase the chance of members attending in person and to minimise travel costs. On average about 6-8 members attended meetings in person and the others joined online. The following meetings were held to discuss the direction of the project:• Barcelona, 23 Oct 2012• Helsinki, 6 Jun 2013• London, 22 Jan 2014Minutes and actions were circulated after each meeting, and members were kept updated on progress via e-mail.No change in the structure of the consortium was recorded during the project lifetime, however the leading partner JISC changed its legal status to become a charity company limited by guarantee. Some minor delay with the administrative procedure for modifying the partner status with the EC was experienced, followed by delays with processing payments within JISC itself as a result of this transition. Given the late start of the project, significant attempts have been made to recover the initial 4 months delay. However this proved difficult in the context of key project staff being lost by four of the six partners, so the project continued to run behind schedule. It also became clear that the time frame planned for the case studies in the original proposal was too ambitious for enabling worthwhile case studies to be established. Discussion with the EC on this topic resulted in the project being granted a six month extension within the original budget envelope, which allowed it to achieve the objectives set in the project plan.As all partners expressed interest in developing further outputs beyond the funded life of the project, a number of discussions about project sustainability were held with e-IRG, RDA, OpenAire+, Eudat, DataCite, WDS/Codata. One of the preferred avenues for further development was the opportunity to build a RDM self-assessment instrument based on the SIM4RDM frameworks. This would provide users with an easy-to-use tool to measure their RDM maturity and step-by-step guidance on how to improve these levels. Further discussion is needed to decide if enough traction exists for such a follow-on.Potential Impact:The main output of the SIM4RDM project is the Intervention Framework or “cookbook”. This is targeted at various actors in the research data management landscape: Funders, InfrastructureProviders, Institutions (such as Universities or other Research Institutes), Publishers and Researchers. For each actor, the cookbook gives the ideal maturity of research data management practice, an assessment of the current maturity of data management practice and a list of possible actions to take (including joint actions with other actors). The “cookbook” was tested in a number of case studies which had immediate impact on the communities involved. The secondary output of the SIM4RDM are a set of high level recommendations.Through this work, as well as bringing together stakeholders across Europe and internationally, SIM4RDM has determined required interventions that need to be implemented by programmemanagers within Europe. Using the international experience of the partners and input from the network, SIM4RDM provides models for coordinating such interventions to ensure maximumimpact. Coordinating these activities on a pan-European level will be much more effective than just on a national level. Not all member states have the resources to support research data management across a wide range of disciplines; due to increasing internationalisation of research, support structures need to be developed that can cope with projects that have members in several countries. SIM4RDM has established an evaluation frameworks for measuring the impact of the interventions of programme managers. This will make those interventions more effective and ensure they deliver value for money. The frameworks will also be crucial for developing recommendations for interventions on an international and pan-European level.SIM4RDM has enabled funding bodies and specialist organisations aware of relevant activities within Europe and internationally. Raising awareness is of much importance in an area where even specialist organisations have only limited and fragmented knowledge of relevant activities in other member states. Valuable networks are thus being created across Europe and internationally, which help organisations assess their activities in a wider international context in order to coordinate and plan more efficiently. Through the landscape survey and the programme of workshops key stakeholders were brought together for the development of a long-term strategy for supporting research data management across the EU. In line with our activities of collecting and analysing information, the focus was on sharing knowledge and experience in building the support infrastructures for research data management. This will help programme managers better plan their activities in the future, and will deliver savings by increasing the effectiveness of the interventions.Fostering expert communities and providing researchers with better understanding of the issues related to research data will also make it easier for the users of research infrastructure to give feedback as well as to actively formulate their requirements. This will help ensuring that research infrastructures are fit for purpose. With the current focus on publication of research findings, researchers may feel that they are rewarded for not sharing their data, treating data as a treasure that will reap further rewards if kept private. By recommending approaches on how to change that culture, SIM4RDM can contribute to an increase of sharing data. Sharing data can make research more transparent by ensuring that results can more easily be verified. It can also allow interested members of the public to make use of research data. Industry will also be able to benefit by making use of high quality data for research and development, which will increase productivity and innovation.Throughout its lifetime the project aimed to ensure contact and engage with a wide stakeholder community via a website, a newsletter, and a series of events, and to disseminate the project widely and seek input from a range of stakeholders.The project website http://www.sim4rdm.eu provided relevant information and news for the key stakeholder groups, as well as general information about the project. The initial website was redesigned two times as the project progressed, with dedicated sections targeting issues of interest to each stakeholder group, and was updated regularly to incorporate feedback gathered from project workshops. A series of newsletters were published during the project lifetime. They provided updates about the project progress and plans, and highlighted forthcoming events either organized by the project itself, or potentially relevant to stakeholders. The newsletters were circulated via the mailing distribution list, which towards the end of the project counted about 150 subscribers. A flyer highlighting the main project results and recommendations was also produced and distributed to the participants at main project events. A poster showcasing the funding and evaluation frameworks in a visually memorable form was presented at the RDA 3rd Conference in Dublin in March 2014. Presentations and work-in-progress accounts of the project findings were made at several conferences and workshops, as follows:• 9th e-Infrastructure concertation, Lyon, France, 22-23 Sep 2011• e-IRG workshop, Poznan, Poland, 12-13 Oct 2011• NetworkShop2012, Veszprem, Hungary, 11-13 Apr 2012 • 1st EUDAT Conference, Barcelona, Spain, 22-24 Oct 2012• RDA 1st Plenary, Gothenburg, Sweden, 18-20 Mar 2013 • KE Making Data Count workshop, Berlin, Germany, 12 Apr 2013 • 2nd EUDAT Conference, Rome, Italy, 28-30 Oct 2013• RDA 3rd Plenary, Dublin, Ireland, 26-28 Mar 2014Informal discussions and briefings on project progress were also held with representatives of CODATA, Science Europe, Knowledge Exchange, ESFRI, E-IRG and other organizations, mainly during commonly attended events. On various occasions the project members explored opportunities for sustainable development beyond the funded life of the project. This happened in discussions among the project partners, as well as with members of the Advisory Board and representatives of organizations such as e-IRG, RDA, OpenAire+, Eudat, DataCite, WDS/Codata. One of the preferred avenues for future development was the proposal to build a self-assessment tool based on the SIM4RDM frameworks. This would be able to combine in an easy-to-use online tool a self-assessment benchmark to evaluate one's RDM maturity against the levels of other relevant players with a RDM “cookbook” providing step-by-step guidance and actions to improve these levels. These incipient consultations indicated a significant level of interest in this opportunity. Further discussion is needed to identify the players that are willing to take the lead in developing such a follow-on.List of Websites:http://www.sim4rdm.euContact: Matthew Dovey, Jisc, Lumen House, Library Avenue, Harwell Oxford, Didcot, OX11 0SG (matthew.dovey@jisc.ac.uk) Documentos relacionados final1-sim4rdmselfassessmenttoolposter.pdf
