Tag women in STEM

Participate in the European Digital Skills Certificate (EDSC) feasibility study!

Anastasija Nikiforova Leave a comment

📣📣📣 This is a short post to let you know that we have reached the most important phase of the consultation process for the European Digital Skills Certificate (#EDSC) feasibility study, and on behalf of EDSC – as EDSC Ambassador – I sincerely invite Public authorities, Policy makers, Education & Training providers working in the fields of education, digitalisation, and employment, and to education and training providers at the national, regional and/or local level to participate in this last survey.

🎯🎯🎯This survey aims to get an overview of the potential demand for an EDSC, the needs and gaps in the current digital skills certification ecosystem, the expected value and potential benefits of an EDSC, and of the key requirements for an EDSC. 


The European Commission’s Directorate-General for Employment, Social Affairs and Inclusion has initiated Action 9 of the Digital Education Action Plan (DEAP 2021-2017), that is, to develop a European Digital Skills Certificate (EDSC) that may be recognised and accepted by governments, employers, and other stakeholders across Europe. This would allow Europeans to indicate their level of digital competences, corresponding to the Digital Competence Framework (DigComp) proficiency levels.

In order to participate, you have to first register. If you are not yet registered, please fill in the form available at the link: https://edsc-consultation.eu/register/ 

The survey takes around 30 minutes,  and it will be open until the 7th of April 2023. 

Thank you in advance for taking the time to complete the questionnaire! 

Our – EOSC TF “FAIR Metrics and Data Quality” paper “Towards a data quality framework for EOSC” is released!🍷🍷🍷

Anastasija Nikiforova Leave a comment

I am glad to announce the release of “Towards a data quality framework for EOSC” document, which we have been hard at work on hard for several months as the Data Quality subgroup of the “FAIR Metrics and Data Quality” Task Force European Open ScienceCloud (EOSC) Association) – Carlo Lacagnina, Romain David, Anastasija Nikiforova, Mari Elisa Kuusniemi, Cinzia Cappiello, Oliver Biehlmaier, Louise Wright, Chris Schubert, Andrea Bertino, Hannes Thiemann, Richard Dennis.

This document explains basic concepts to build a solid basis for a mutual understanding of data quality in a multidisciplinary environment such as EOSC. These range from the difference between quality control, assurance, and management to categories of quality dimensions, as well as typical approaches and workflows to curate and disseminate dataset quality information, minimum requirements, indicators, certification, and vocabulary. These concepts are explored considering the importance of evaluating resources carefully when deciding the sophistication of the quality assessments. Human resources, technology capabilities, and capacity-building plans constrain the design of sustainable solutions. Distilling the knowledge accumulated in this Task Force, we extracted cross-domain commonalities (each TF member brings his / her own experience and knowledge – we all represent different domains and therefore try to make our contributions domain-agnostic, but at the same time considering every nuance that our specialism can bring and what deserves to be heard by others), as well as lessons learned, and challenges.

The resulting main recommendations are:

  1. Data quality assessment needs standards to check data against; unfortunately, not all communities have agreed on standards, so EOSC should assist and push each community to agree on community standards to guarantee the FAIR exchange of research data. Although we extracted a few examples highlighting this gap, the current situation requires a more detailed and systematic evaluation in each community. Establishing a quality management function can help in this direction because the process can identify which standard already in use by some initiatives can be enforced as a general requirement for that community. We recommend that EOSC considers taking the opportunity to encourage communities to reach a consensus in using their standards.
  2. Data in EOSC need to be served with enough information for the user to understand how to read and correctly interpret the dataset, what restrictions are in place to use it, and what processes participate in its production. EOSC should ensure that the dataset is structured and documented in a way that can be (re)used and understood. Quality assessments in EOSC should not be concerned with checking the soundness of the data content. Aspects like uncertainty are also important to properly (re)use a dataset. Still, these aspects must be evaluated outside the EOSC ecosystem, which only checks that evidence about data content assessments is available. Following stakeholders’ expectations, we recommend that EOSC is equipped with essential data quality management, i.e., it should perform tasks like controlling the availability of basic metadata and documentation and performing basic metadata compliance checks. The EOSC quality management should not change data but point to deficiencies that the data provider or producer can address.
  3. Errors found by the curators or users need to be rectified by the data producer/provider. If not possible, errors need to be documented. Improving data quality as close to the source (i.e., producer or provider) as possible is highly recommended. Quality assessments conducted in EOSC should be shown first to the data provider to give a chance to improve the data and then to the users.
  4. User engagement is necessary to understand the user requirements (needs, expectations, etc.); it may or may not be part of a quality management function. Determining and evaluating stakeholder needs is not a one-time requirement but a continuous and collaborative part of the service delivery process.
  5. It is recommended to develop a proof-of-concept quality function performing basic quality assessments tailored to the EOSC needs (e.g., data reliability and usability). These assessments can also support rewarding research teams most committed to providing FAIR datasets. The proof-of-concept function cannot be a theoretical conceptualization of what is preferable in terms of quality. Still, it must be constrained by the reality of dealing with an enormous amount of data within a reasonable time and workforce.
  6. Data quality is a concern for all stakeholders, detailed further in this document. The quality assessments must be a multi-actor process between the data provider, EOSC, and users, potentially extended to other actors in the long run. The resulting content of quality assessments should be captured in structured, human- and machine-readable, and standard-based formats. Dataset information must be easily comparable across similar products, which calls for providing homogeneous quality information.
  7. A number of requirements valid for all datasets in EOSC (and beyond) and specific aspects of a maturity matrix gauging the maturity of a community when dealing with quality have been defined. Further refinement will be necessary for the future, and specific standards to follow will need to be identified.

We sincerely invite you to take a look at this very concise 76-pages long overview of the topic and look forward to your recommendations / suggestions / feedback – we hope to provide you with the opportunity to communicate the above conveniently very soon, so take your time to read, while we are making our last preparations 📖 🍷📖🍷📖🍷 But make sure you have a glass of wine at the time of reading it, as this will make sense at some point of reading, i.e. when we compare data quality with wine quality with reference to both flavour type and intensity (intrinsic quality), brand, packaging (extrinsic quality)… but no more teasers and bon appetite! 🍷🍷🍷
The document can be found in an Open Access here.

We also want to acknowledge the contribution and input of colleagues from several European institutions, the EOSC Association and several external-to-TF stakeholders who gave feedback based on their own experience, and the TF Support Officer Paola Ronzino, as well as to our colleagues – Sarah Stryeck and Raed Al-Zoubi, and the last but not the list – to all respondents and everyone involved.

Wrapping up 2022

Anastasija Nikiforova Leave a comment

While 2022 can be characterized by many challenges that each and every person and society as a whole faced, at the end of this year, I would like to refer to only the positive things it has brought me, for which I am exceptionally grateful! This year has been really full of very different events and experiences, so it is great to take a second and realize what has happened so far – in such a dynamic world, it is sometimes difficult to keep track of everything that has happened in a certain period of time, so it is worthwhile doing it for yourself!

Image source: CBC news

Probably the first thing that comes in mind is a big change that took place in my life, i.e. the fact that this year I joined University of Tartu (Faculty of Science and Technology, Institute of Computer Science, Chair of Software Engineering)) as an assistant professor of Information Systems.

At the same time, I had a great experience of acting as a visiting researcher at the Delft University of Technology, Faculty of Technology, Policy and Management. This was a 6-months long research “visit”, which due to COVID-19 pandemic, however, took place online, although I still got a nice experience, including communication with many TU Delft colleagues, including discussions that we established during my participation in a monthly ICT colloquium, one of which was dedicated to my research. During this research visit, together with my esteemed colleague Anneke Zuiderwijk we launched a study in which we revisit the barriers associated with the publication of government data as OGD by public agencies, not only because it is a dynamic topic, where factors related to the intent and resistance to this tend to change, but also because the pandemic has changed views on the value and usefulness of the OGD, with the reference to both perspective – provision and usage. Thus, we believe that these factors have changed. Considering the role of the OGD in the current society, we decided not to use almost “traditional” models such as TAM, UTAT, TOE etc., but to refer to another theory not previously used in e-government area, namely the Innovation Resistance Theory (IRT), which, however, has proved to be very useful in the field of business and management (though not only this discipline). Thus, the objective of this research is twofold – to test the appropriateness/ validity of this theory for the OGD and e-gov domains, as well as to revisit the barriers to publishing government data as an OGD, also checking whether COVID-19 has changed the state of affairs in this regard significantly. So far we have come up with the OGD-adapted IRT model, which we presented at ICEGOV2022, which was recognized as one of three best papers, nominated for best paper awards, which was an amazing conclusion to my “visit” to TU Delft. The study, however, continues even after the end of this visit.

To improve my skills and knowledge in areas of interest to me, this year I also attended two Summer Schools – 6th International Summer School on the Deep Learning called DeepLearn 2022, and the 9th International Summer School On Open and Collaborative Governance that took place in conjunction with the 12th Samos 2022 Summit on ICT-Enabled Governance.

But, of course, I tried not only to acquire and develop new knowledge and skills, but also to share them with others, including both my students, colleagues, students of my foreign colleagues, pupils, school teachers, industry and others. In addition, I was honored to participate in several events, acting as both the keynote, panelist, invited speaker, expert, guest of honor, and as a regular speaker discussing the hot topics, and presenting my own works. All in all, it was a busy and eventful year.

Even more, my work results were recognized and awarded several times this year – I was named and awarded as best moderator of Research and Innovation Forum 2022, got nominated for the best paper award of ICEGOV2022 – 15th International Conference on Theory and Practice of Electronic Governance, and got the best paper award of KMIS2022 – 14th International Conference on Knowledge Management and Information Systems in conjunction with the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

I also was invited to join Editorial Board of several journals and was pleased to accept their kind invitation. All in all, starting with this year I am an Editorial Board member of eJournal of eDemocracy and Open Government (JeDEM), Area Editor of “Focus on Data-driven Transformations in Policy and Governance” for Data & Policy (Cambridge University Press), Politics of Technology section of Frontiers in Political Science. In addition, I served as an organizing and program committee for several conferences, acting as general co-chair for EGETC – Electronic Governance with Emerging Technologies Conference, part of organizing team for Data for Policy 2022 devoted to ecosystem of innovation and virtual-physical interaction, publicity chair for IDSTA – International Conference on Intelligent Data Science Technologies and Applications and MegaData – International Workshop on Advanced Data Systems Management, Engineering, and Analytics as part of IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID2023), session chair for KMIS (sessions “Big Data and Analytics” and “Knowledge management Strategies and Implementations”), IDSTA, and panel moderator for RiiForum – Research and Innovation Forum 2022, when for moderating the “Business in the era of pervasive digitalization” panel, I was awarded with the best panel moderator award.

Another activity that is closely related to the topic I am proud of, is the series of workshops I launched together with my colleagues devoted to the identification of determinants for identifying High-value Datasets titled “Identification of high-value dataset determinants: is there a silver bullet?“. The idea of referring to this topic came to my mind a long time ago, when the VARAM ministry of Latvia responsible for the OGD initiative and development and maintaining the OGD portal reached me first as one of people who could contribute to prioritization of the datasets to be potentially opened, and later with the reference to the concept of HVD. After conducting an analysis for Latvia, I decided to refer to this topic from a more scientific point of view, and now we have already two editions of the workshop in the pocket – one that took place during ICEGOV2022, and another one – as part of ICOD2022, where we managed to have interactive sessions with ~40 open data researchers and/or experts and brainstorm on this topic (read more).

As regards the role of PC member, I was honored to be invited to become such for several conferences, including: EGOV2022 – IFIP EGOV-CeDEM-EPART 2022 in conjunction with 23rd Annual International Conference on Digital Government Research”, Data for Policy 2022, ADBIS 2022 – 26th European Conference on Advances in Databases and Information Systems (Workshop on Advances Data Systems Management, Engineering, and Analytics), EGETC2022 – Electronic Governance with Emerging Technologies Conference, ICT2022 – International Conference on ICT, Society and Human Beings as part of the Multi Conference on Computer Science and Information Systems (MCCSIS2022), IHCI2022 – International Conference on Interfaces and Human Computer Interaction” also part of MCCSIS2022, IDSTA2022 – The International Conference on Intelligent Data Science Technologies and Applications, iLRN2022 – The International Conference of the Immersive Learning Research Network, RiiForum2022 – Research and Innovation Forum 2022, FedCSIS2022 / ISM2022 – Conference on Information Systems Management as part of the Conference on Computer Science and Intelligence Systems, ESWC2022 – International Workshop on Knowledge Graph Generation from Text (Text2KG) co-located with the Extended Semantic Web Conference, KGSWC2022 – Iberoamerican Knowledge Graph and Semantic Web. In addition, I try my best to find time for reviewing journal articles in top-level journals, when I am invited as an external reviewer. Although these activities take time, but those who are also doing this will definitely confirm that this is an exceptional opportunity to be used not only to provide the colleagues with an external view on the article and suggest how it could be improved, but also identify best-practices in writing and presenting ideas, identifying how your own works can be improved by either following these practices or avoiding them. Thus, I value these opportunities very much and try to find time to devote myself to this, particularly, if I understand that my input – review can be of value for authors. Here, at least a few journals that definitely deserved my gratitude are Technological Forecasting and Social Change, Government Information Quarterly, Technology in Society (Elsevier), Digital Policy, Regulation and Governance, Transforming Government: People, Process and Policy, Information and Learning Sciences, Online Information Review (Emerald), Scientific Data (Springer Nature), eJournal of eDemocracy and Open Government (JeDEM), International Journal of Human-Computer Interaction (IJHC), but actually all of those, where I contributed 🙂

And since I referred to both journals and conferences I was related to this year, it is the time to refer to my own contributions, i.e. some quantitative indicators.

This year 23 articles, including 3 book chapters, one extended abstract and one whitepaper were published, authored by me together with my colleagues, while some of them even with my students (some of them will be officially published in 2023, same as a few were written in 2021). 10 of them are journal articles, one – whitepaper published by European Commission, and 9 – conference papers:

The first study listed above, i.e. “Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities” (Lnenicka, Nikiforova, Luterek, Azeroual, Dandison, Valtenbergs, Machova) was noticed by the Living Library that seeks to provide actionable knowledge on governance innovation, informing and inspiring policymakers, practitioners, technologists, and researchers working at the intersection of governance, innovation, and technology in a timely, digestible and comprehensive manner, identifying the “signal in the noise” by curating research, best practices, points of view, new tools, and developments… Proud to see our joint article there (read more…)

Similarly, The Open Data Institute hosted micro-site on the exploration of the future of data portals and citizen engagement (led by Rachel Wilson, in collaboration with ODI Associate Consultant Tim Davies) publishes a series of very insightful posts reflecting on the most prospective studies to take stock of the state of portals, and explore possible futures, where two of them reflect on my previous studies, namely Transparency-by-design: What is the role of open data portals? (Lnenicka, M. and Nikiforova, A. 2021, Telematics and Informatics 61), Open government data portal usability: A user-centred usability analysis of 41 open government data portals (Nikiforova & McBride, Telematics and Informatics), Benchmarking open data efforts through indices and rankings: Assessing development and contexts of use (Lnenicka, Luterek & Nikiforova, Telematics and Informatics), Timeliness of Open Data in Open Government Data Portals Through Pandemic-related Data: A long data way from the publisher to the user (Nikiforova, 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA))! (read more#1…) (read more#2…)

This year I participated in 10 international conferences, where 9 papers (co-)authored by me were presented, with 2 conferences, where I chaired my (co-)organized workshops, with another conference, where I acted as a keynote speaker, and some more other events of more national and/or regional nature. Some of them are:

From the above, let me emphasize one event, which was very specific for me since it was my first experience as a panelist, especially in such a “crowded” event (due to a very high rate of the attendance) – ONE Conference 2022 (Health, Environment, Society), which took place between June 21 and 24, Brussels, Belgium. It was co-organised by European Food Safety Authority (EFSA) and its European sister agencies European Environment Agency, European Medicines Agency, European Chemicals Agency, European Centre for Disease Prevention and Control (ECDC), but if you are an active follower of my blog, you know this already (I posted about this event previously). As a person representing not only academia, but also EOSC (European Open Science Cloud) and dealing with the topics of data quality and open data, I was invited to serves as a panelist of the “ONE society” thematic track, panel discussion “Turning open science into practice: causality as a showcase”. It was a very nice experience and opportunity for sharing our experience on obstacles, benefits and the feasibility of adopting open science approaches, and elaborate on the following questions (although they were more but these one are my favorites): Can the use of open science increase trust to regulatory science? Or does it increase the risk to lose focus, introduce conflicting interests and, thus, threaten reputation? What are the barriers to make open science viable in support to the scientific assessment process carried out by public organizations? What are the tools/ methods available enabling, supporting and sustaining long term open science initiatives today and what could be envisaged for the future? Do we need a governance to handle open data in support to scientific assessment processes carried out by regulatory science bodies? How the data coming from different sources can be harmonized making it appropriate for further use and combination?

And as a follow-up for this event, I was kindly invited by EFSA to contribute to setting the scene on the concept of ‘standards for data exchange’, ‘standards for data content’ and ‘standards for data generation’ as part of European Food Safety Authority (EFSA) and Evidence-Based Toxicology Collaboration (EBTC) ongoing project on the creation of a standard for data exchange in support of automation of Systematic Review (as the answer to the call made in “Roadmap for actions on artificial intelligence for evidence management in risk assessment”). It was really nice to know that what we are doing in EOSC Association (Task Force “FAIR metrics and data quality”) is of interest for our colleagues from EFSA and EBTC. Also, it was super nice to listen other points of view and get involved in the discussion with other speakers and organisers and I am looking forward the first draft expected to be ready by the end of this year.

Since this is so much about the open science, as well as I already mentioned EOSC, probably it is worthwhile to mention that we just got published our – EOSC Task Force on FAIR Metrics and Data Quality whitepaper “Community-driven Governance of FAIRness Assessment: An Open Issue, an Open Discussion” (Mark D. Wilkinson; Susanna-Assunta Sansone; Eva Méndez; Romain David; Richard Dennis; David Hecker; Mari Kleemola; Carlo Lacagnina; Anastasija Nikiforova; Leyla Jael Castro), which is published by European Commission, of course, in an open access, here. In it we emphasize that although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners – and provides examples outlining specific stakeholders’ (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation, while more detail on both the whitepaper, as well as how to get engaged in this conversation, you can find here.

Here, let me also mention another activity – Guest Lectures, which this year I delivered to students of the Federal University of Technology – Parana (UTFPR, Brazil) and, more precisely so-called PPGEP program – Postgraduate Program in Production Engineering (port. Programa de Pós-Graduação em Engenharia de Produção), and to students of University of South-Eastern Norway (USN) – this was already my second time of delivering a guest lecture for USN. The first lecture was titled “The role of open data in the development of sustainable smart cities and smart society“, in scope of which I was pleasured to raise a discussion on three topics of particular interest – open data, Smart City, and Society 5.0, which are actually very interrelated, while the second – “Open data as a catalyst for collaborative, data-driven smart cities and smart society: what is the key to success?”. Both lectures inspired me a lot since were accompanied with a lively discussion around touched topics, which is always a pleasure for the lecturer.

In addition to some lectures delivered to actual students, some of my talks were delivered to people outside academia as well.

As an example, in February I got yet another experience by participating in a programme launched by Riga TechGirls and supported by Google.org (“Google Impact challenge” grant), in addition to local supporters such as the Ministry of Education and Science of Latvia, the Ministry of Culture, Riga city council (Rīgas Dome), titled “Human on technology” for more than 2000 Latvian teachers with the aim of disrupting technophobia and provide them with digital skills that are “must-have” in this digital world/ era. I have acted as both the lecturer and the lead mentor for the digital development workshop held as a part of the “Information and data literacy” module (read more…)

While the above event was dedicated to adults, another experience was to work with pupils representing Generation Z – this year, although same as in previous years, I have been a mentor of the Latvian Open Data Hackathon and an idea generator for pupils, organized by the Latvian Open Technologies Association with the support of DATI Group, E-Klase, Latvijas Kultūras akadēmija / Latvian Academy of Culture, Vides aizsardzības un reģionālās attīstības ministrija (VARAM)/ Ministry of Environmental Protection and Regional Development of Republic of Latvia and others. This year the main topic of the hackathon was cultural heritage, where within a month, 36 teams from 126 participants from all over Latvia developed their ideas and prototypes, 10 teams reached the final after a round of semi-final presentations of their solutions to us – the mentor team (of course, we worked with the assigned teams in previous weeks as well).  Here, we not only evaluated these ideas, but also provided them with yet another portion of feedback and suggestions for improving the idea or prototype for its further presentation in the final, where the jury will finally decide who the winner is. The participants surprised us (mentors) very much both with the diversity of ideas and in very many times with their technical knowledge and skills (AI, crowdsourcing, gamification to name just a few) – just wow!

In the continuation of the topic of hackathon, I am interested in, researching it a bit as well, I also participated in the Hack the hackathon (Vol. 2) workshop organized by the Flatiron Institute (New York, NY, USA), the purpose of which was to bring together researchers of different disciplines studying hackathons and hackathon practitioners from different communities to meet and discuss the current state of practice and research around hackathons as well as future challenges. I also had the honor of being one of the participants, who was invited to deliver a short talk on practical experience within a topic to be further discussed and brainstormed by all of us, which, obviously, was related to the above topic and was entitled “Gen Z hackathons: digital natives for hackathons or hackathons for digital natives?”. Unfortunately, considering my schedule at that point, when I really needed Time-Turner, I did not managed to dive into this event in the way I wanted to (even considering the opportunity to participate online, which I used), but this was still a very lively event, full of emotions (positive)!

And as I mentioned before, another “set” of activities were related to the industry. Here, there are three events that I enjoyed very much, namely:

  • “Virtual Brown Bag Lunch Talks” intended for the Information Technologies, Manufacturing, and Engineering Employees in Companies associated with Index Manufacturing Association, where I was invited to delivered a talk “Data Security as a top priority or what Internet of Things (IoT) Search engines know about you“, which is based on several studies conducted by me before. Probably the most interesting point to be mentioned that this event was intended for Mexican audience, which was definitely something new for me. We had an exceptionally interesting discussion after my talk with representatives of the industry to whom these events are made, and I was super delighted to get so many positive comments, which definitely makes this event something to be in the list;
  • another very interesting “foreign” experience I had is related to the Babu Banarasi Das University (BBDU, Department of Computer Science and Engineering) Development Program «Artificial Intelligence for Sustainable Development» organized by AI Research Centre, Department of Computer Science & Engineering, Babu Banarasi Das University (India), ShodhGuru Research Labs, Soft Computing Research Society, IEEE UP Section, Computational Intelligence Society Chapter, where I was invited to deliver a talk, which I decided to devote to two topics I am interested in, which I titled “Artificial Intelligence for Open Data or Open Data for Artificial Intelligence?”. While previous event was based in Mexico (I participate online, of course), this one was intended for India and Indian representatives from industry interested in advances in the field of Artificial Intelligence, which were more than 130 people. In this talk, I not only provided an insights on both topics, and what can opportunities the combination of these pehnomenons provide us with, but also about the other side of the coin, i.e., this “magic duo” is not always about “unicorns and ice creams“, where the current state-of-the-art suggests that open data my pose also certain risks (read more here);
  • continuing this “journey”, this summer, while participating in a Summer school on e-government I referred to previously, I also had a pleasure to participate in one more exceptionally interested event – “Integration of open data and artificial intelligence in the development of smart cities in Africa” workshop organized as part of the African Cities Lab Project conducted by representatives of both academia, industry and government from Morocco, Ghana, Tunisia, South Africa, Rwanda, Benin, Switzerland, where I was invited as a keynote speaker and delivered the talk “Open data and crowdsourced data as enablers and drivers for smart African cities”. Again, after the talk we had an extremely interesting discussion, when the discussion about how to develop the OGD initiative in African cities, where the support for this is very limited, we managed to raise very interesting questions and I came to several new ideas, about which I have never thought before, for which I am very grateful to those participants, who were actively involved in this discussion!
  • but, of course, one local event I enjoyed very much should also be mentioned here –  Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“ seminar organized by the Institute of Computer Science (University of Tartu) in cooperation with Swedbank, in which the importance of BI with some focus on data quality was discussed. The seminar consisted of four talks, which were followed by a panel moderated by my colleague prof. Marlon Dumas – 2 talks were delivered by representatives of the University of Tartu, where we both decided to focus our talks on data quality. Here I was invited to deliver a talk on one of studies I was recently involved in, and I titled it – “Data Lake or Data Warehouse? Data cleaning or data wrangling? How to ensure the quality of your data?“. Again, the discussions followed after the talk and also a discussion established as part fof the panel we had were both incredibely interesting and allowed us to exchange our ideas, experience and thought on the future development of related concepts, which is probably the best outcome of any event (read more).

This is a short overview of the activities carried out and the events in which I took part this year. As follows from the variety of these events, I met many people (virtually and physically), some of them became my colleagues, others – also friends. All in all, this is also about people. People who support you, people who believe in you, and people who respect you and whom you respect. My wish to myself and all of you is to have only such people around – those who respect you, whom you respect (very much), those who support you, and not only if there is an urgent need for this support, but simply because they want to be there and provide you with their continuous support, those who not only respect your current works and achievements, but those who believe that you can and will definitely be able achieve even more!

And the last thing to say here, of course, is – thank you, 2022 for all those positive things and emotions you brought, and bye! Welcome 2023!!!

Our – EOSC TF “FAIR Metrics and Data Quality” – whitepaper “Community-driven Governance of FAIRness Assessment: An Open Issue, an Open Discussion” is published by European Commission!

Anastasija Nikiforova Leave a comment

Proud to be part of the EOSC Task Force on FAIR Metrics and Data Quality and present our whitepaper “Community-driven Governance of FAIRness Assessment: An Open Issue, an Open Discussion” (Mark D. Wilkinson; Susanna-Assunta Sansone; Eva Méndez; Romain David; Richard Dennis; David Hecker; Mari Kleemola; Carlo Lacagnina; Anastasija Nikiforova; Leyla Jael Castro) published by European Commission, of course, in an open access, here.

“Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable.

The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral.

FAIR is evolving in some expected and some unexpected ways. FAIR “Reusability” sub-principle R1.3 states that “(meta)data should meet domain-relevant community standards,” which predicts a proliferation of FAIR interpretations by individual communities as they select their preferred approach to FAIRness. Similarly, as expected, there is an active movement around the adaptation of the FAIR Principles to digital objects other than data (e.g., software and workflows), again with individual communities interpreting what FAIRness means in these expanded contexts. However, there have also been attempts to expand the FAIR Principles themselves in recent years, including features of digital objects beyond reusability, including popularity (reuse/citation), reproducibility, reliability, data quality, etc. All of this is occurring with no overall coordination or planning.

A range of FAIR assessment metrics and tools have been designed that measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected
behaviours. While it is anticipated that communities will define domain-specific FAIR metrics and tests, it is desirable to avoid “gaming the system” and have broadly agreed-upon approaches to FAIRness that do not favour a specific implementation of technology.

These observations suggest a growing need to align the different interpretations of the FAIR Principles. However, this whitepaper does not suggest that the FAIR Principles themselves require governance. Indeed, the document argues that the Principles should remain untouched. Specialised communities should extend/edit those Principles to adapt and make them more relevant to their community and their specific research outcome intended to be FAIR.

This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners – and provides examples outlining specific stakeholders’ (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable”

Cite as: Mark D. Wilkinson, Susanna-Assunta Sansone, Eva Méndez, Romain David, Richard Dennis, David Hecker, Mari Kleemola, Carlo Lacagnina, Anastasija Nikiforova, & Leyla Jael Castro. (2022). Community-driven Governance of FAIRness Assessment: An Open Issue, an Open Discussion [version 1; peer review: awaiting peer review]. Open Res Europe 2022, 2:146 (https://doi.org/10.12688/openreseurope.15364.1)

Want to know more? Read more here!

KMIS2022_Best_Paper_Award

14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K): how it was and who got the Best Paper Award?

Anastasija Nikiforova Leave a comment

In this post I would like to briefly elaborate on a truly insightful 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), where I was honored to participate as a speaker, presenting our paper “Putting FAIR principles in the context of research information: FAIRness for CRIS and CRIS for FAIRness” (authors: Otmane Azeroual, Joachim Schopfel and Janne Polonen, and Anastasija Nikiforova), and as a chair of two absolutely amazing sessions, where live and fruitful discussions took place, which is a real indicator of the success of such! And spoiler, our paper was recognized as the Best Paper! (i.e., best paper award goes to… :))

IC3K consists of three subconferences, namely 14th International Conference on Knowledge Discovery and Information Retrieval (KDIR), 14th International Conference on Knowledge Engineering and Ontology Development (KEOD), and 14th International Conference on Knowledge Management and Information Systems (KMIS), where the latter is the one, to which my paper has been accepted, and also won the Best Paper Award – I know, this is a repetition, but I am glad to receive it, same as the euroCRIS community is proud for us – read more here…!

Briefly about our study, with which we mostly wanted to urge a call for action in the area of CRIS and their FAIRness. Of course, this is all about the digitization, which take place in various domain, including but not limited to the research domain, where it refers to the increasing integration and analysis of research information as part of the research data management process. However, it is not clear whether this research information is actually used and, more importantly, whether this information and data are of sufficient quality, and value and knowledge could be extracted from them. It is considered that FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication (by one of the colleagues I work together in European Open Science Cloud), they have rapidly proliferated and have become part of both national and international research funding programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data and their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS, also known as RIS, RIMS), which is an underrepresented subject for research, is the subject of our study. What should be kept in mind is that the research information is not just research data, and research information management systems such as CRIS are not just repositories for research data. They are much more complex, alive, dynamic, interactive and multi-stakeholder objects. However, in the real-world they are not directly subject to the FAIR research data management guiding principles. Thus, supporting the call for the need for a ”one-stop-shop and register-once use-many approach”, we argue that CRIS is a key component of the research infrastructure landscape / ecosystem, directly targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS. All in all, three propositions on which we elaborate in our paper and invite  everyone representing this domain to think of, are:

1. research information management systems (CRIS) are helpful to assess the FAIRness of research data and data repositories;

2. research information management systems (CRIS) contribute to the FAIRness of other research infrastructure;

3. research information management systems (CRIS) can be improved through the application of the FAIR principles.

Here, we have raised a discussion on this topic showing that the improvement of FAIRness is a dual or bidirectional process, where CRIS promotes and contributes to the FAIRness of data and infrastructures, and FAIR principles push for further improvement in the underlying CRIS data model and format, positively affecting the sustainability of these systems and underlying artifacts. CRIS are beneficial for FAIR, and FAIR is beneficial for CRIS. Nevertheless, as pointed out by (Tatum and Brown, 2018), the impact of CRIS on FAIRness is mainly focused on the (1) findability (“F” in FAIR) through the use of persistent identifiers and (2) interoperability (“I” in FAIR) through standard metadata, while the impact on the other two principles, namely accessibility and reusability (“A” and “R” in FAIR) seems to be more indirect, related to and conditioned by metadata on licensing and access. Paraphrasing the statement that “FAIRness is necessary, but not sufficient for ‘open’” (Tatum and Brown, 2018), our conclusion is that “CRIS are necessary but not sufficient for FAIRness”.

This study differs significantly from what I typically talk about, but it was to contribute to it, thereby sharing the experience I gain in European Open Science Cloud (EOSC), and respective Task Force I am involved in – “FAIR metrics and data quality”. It also allowed me to provide some insights on what we are dealing with within this domain and how our activities contribute to the currently limited body of knowledge on this topic.

Putting FAIR principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRnessDownload

A bit about the sessions I chaired and topics raised within them, which were very diverse but equally relevant and interesting. I was kindly invited to chair two sessions, namely “Big Data and Analytics” and “Knowledge management Strategies and Implementations”, where the papers on the following topics were presented:

  • Decision Support for Production Control based on Machine Learning by Simulation-generated Data (Konstantin Muehlbauer, Lukas Rissmann, Sebastian Meissner, Landshut University of Applied Sciences, Germany);
  • Exploring the Test Driven Development of a Fraud Detection Application using the Google Cloud Platform (Daniel Staegemann, Matthias Volk, Maneendra Perera, Klaus Turowski, Otto-von-Guericke University Magdeburg, Germany) – this paper was also recognized as the best student paper;
  • Decision Making with Clustered Majority Judgment (Emanuele D’ajello , Davide Formica, Elio Masciari, Gaia Mattia, Arianna Anniciello, Cristina Moscariello, Stefano Quintarelli, Davide Zaccarella, University of Napoli Federico II, Copernicani, Milano, Italy.
  • Virtual Reality (VR) Technology Integration in the Training Environment Leads to Behaviour Change (Amy Rosellini, University of North Texas, USA)
  • Innovation in Boutique Hotels in Valletta, Malta: A Multi-level Investigation (Kristina, University of Malta, Malta)

And, of course, as is the case for each and every conference, the keynotes are panelists are those, who gather the highest number of attendees, which is obvious, considering the topic they elaborate on, as well as the topics they raise and discuss. IC3K is not an exception, and the conference started with a very insightful discussion on Current Data Security Regulations and the discussion on whether they Serve or rather Restrict the Application of the Tools and Techniques of AI. Each of three speakers, namely Catholijn Jonker, Bart Verheijen, and Giancarlo Guizzardi, presented their views considering the domain they represent. As a result, both were very different, but at the same time leading you to “I cannot agree more” feeling!

One of panelists – Catholijn Jonker (TU Delft) delivered then an absolutely exceptional keynote speech on Self-Reflective Hybrid Intelligence: Combining Human with Artificial Intelligence and Logic. Enjoyed not only the content, but also the style, where the propositions are critically elaborated on, pointing out that they are not indented to serve as a silver bullet, and the scope, as well as side-effects should be determined and considered. Truly insightful and, I would say, inspiring talk.

All in all, thank you, organizers – INSTICC (Institute for Systems and Technologies of Information, Control and Communication), for bringing us together!