Reviewerly/exHarmony huggingface dataset:
The Influence of Continuing Professional Development (CPD) on the Teaching Approaches of secondary School teachers in District Sukkur, Sindh, Pakistan

This paper presents a study conducted in district Sukkur, Sindh, which explores the influence of continuing professional development (CPD) on the teaching approaches of secondary school teachers. The research aims to determine the impact of CPD programs on teachers' knowledge, skills, and practices. The study employs a mixed-method approach, gathering data from 30 head teachers and 120 secondary school teachers who participated in CPD programs. Data collection methods include surveys, interviews, and classroom observations. CPD programs enhance teachers' approaches by improving knowledge, skills, and confidence. They provide new strategies, resources, and tools for pedagogical improvement, fostering collaborative learning, professionalism, and continuous improvement. This leads to improved academic outcomes. Regression analysis shows that CPD programs have a significant positive effect on students' academic outcomes (β = 0.269, p &lt; 0.05). The study highlights challenges faced by teachers in CPD programs and recommends increased government and institutional support for professional development in Sukkur district, Sindh, Pakistan.
Premature convergence in morphology and control co-evolution: a study

This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.
Data Trustees – They Do Work! The Example of Research Data Centers

This contribution presents the long established system of accredited Research Data Centers (RDCs). Created in the data-landscape of the social, behavioral, educational, and economic sciences, they enable access to restricted data and bridge interests of data providers and researchers. A distinctive feature of most research data in the above disciplines is the coverage of real persons. Such sensitive data require specific safeguards. The focus of this contribution is on the institutionalized processes of connecting and securing appropriate research data management strategies for this sort of data. It includes quality assurance measures through the accreditation of RDCs, a monitoring system and the regular cooperation of accredited RDCs.
Isn’t a Number and a URL Enough?

Persistent identifiers (PIDs) are an integral part of research data management and can be found throughout the entire lifecycle of research data. However, their ability to function – to ensure persistence – depends on numerous factors: technical infrastructure, international standards and best practices and their dissemination, agreements on long-term governance of infrastructures, etc. Their applicability is diverse and requires adaptation to the resources and entities referenced by them. The paper describes two projects – PID4NFDI and PID Network Germany – that aim to address these challenges.
Building the Next Generation of Data Savvy Biomedical Researchers

Modern research data management in biomedicine requires data literacy skills. The NFDI4Health consortium, the national research data infrastructure for personal health data, addresses this need with several different training concepts for clinical and epidemiological scientists. Both the institutional anchorage of the training and the thematic focus vary and can be assembled from a modular system as required. The aim is to enable multipliers to adapt and use the training modules through open educational resources. In addition, a competency profile for data stewards is under development to support the choice of required training in research institutions. The results will be fed into the NFDI “Education &amp; Training” Section and will contribute to the Data Literacy Alliance in the future.
Harmonising, Harvesting, and Searching Metadata Across a Repository Federation

The collection of metadata for research data is an important aspect in the FAIR principles. The schema.org and Bioschemas initiatives created a vocabulary to embed markup for many different types, including BioChemEntity, ChemicalSubstance, Gene, MolecularEntity, Protein, and others relevant in the Natural and Life Sciences with immediate benefits for findability of data packages. To bridge the gap between the worlds of semantic-web-driven JSON+LD metadata on the one hand, and established but separately developed interface services in libraries, we have designed an architecture for harmonising, federating and harvesting metadata from several resources. Our approach is to serve JSON+LD embedded in an XML container through a central OAI-Provider. Several resources in NFDI4Chem provide such domain-specific metadata. The CKAN-based NFDI4Chem search service can harvest this metadata using an OAI-PMH harvester extension that can extract the XML-encapsulated JSON+LD metadata, and has search capabilities relevant in the chemistry domain. We invite the community to collaborate and reach a critical mass of providers and consumers in the NFDI.
The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI

The Strategic Research and Innovation Agenda (SRIA) of the European Commission identifies Knowledge Graphs (KGs) as one of the most important technologies for building an interoperability framework and enabling data exchange among users across countries, sectors, and disciplines [1]. KG is a graph-structured knowledge base containing a terminology (vocabulary or ontology) and data entities interrelated via the terminology [2]. KGs are based on semantic web technologies (RDF, SPARQL, etc.) and often used for agile data integration. KGs also play an essential role within Germany as a vehicle to connect research data and research-related entities and make those accessible – examples include the GESIS Knowledge Graph Infrastructure, TIB Open Research Knowledge Graph, and GND.network. Furthermore, the Wikidata knowledge graph, maintained by Wikimedia Germany, contains a large number of research-related entities and is widely used in scientific knowledge management in addition to being an important advocacy tool for open data [3]. Extending domain-specific ontology-supported KGs with the multidisciplinary, crowdsourced knowledge in Wikidata KG would enable significant applications. The linking between expert knowledge systems and world knowledge empowers lay persons to benefit from high-quality research data and ultimately contributes to increasing confidence in scientific research in society.
“Hello ELSA, how are you?”

The proposed contribution will shed light on current and future challenges on legal and ethical questions in research data infrastructures. The authors of the proposal will present the work of NFDI’s section on Ethical, Legal and Social Aspects (hereinafter: ELSA), whose aim is to facilitate cross-disciplinary cooperation between the NFDI consortia in the relevant areas of management and re-use of research data.
Overarching Data Management Ecosystem at HZDR

When dealing with research data management, researchers at Helmholtz- Zentrum Dresden – Rossendorf (HZDR) face a variety of systems and tools. These range from the project planning phase (proposal management, data management plans and policies), over documentation during the experiment or simulation campaign, to the publication (collaborative authoring tools, metadata catalogs, publication systems, data repositories). In addition, modern research projects usually are required to interact with a variety of software stacks and workflow management systems to allow comprehensi- ble and FAIR science on the underlying IT infrastructure (HPC, data storage, network file systems, archival). This article first demonstrates the data management systems and services provided at HZDR, followed by an overview of a self-developed guidance system. It is concluded by a real-world example.
Integrating Data Literacy Into University Curricula

The implementation of competencies regarding Research Data Management (RDM) and Data Literacy (DL) into the curricula is a major challenge for the NFDI community. In physics as well as in other disciples, the beginners’ lab courses are the first point in time where students have to deal with data, and are therefore an intuitive point for the integration of first RDM learning objectives. However, the modification of teaching materials is a time-consuming and potentially expensive task. In this contribution the authors show how this can be achieved in the physics beginners’ lab on the basis of concrete examples, thereby affecting the education of students in a wide range of subjects, including the sciences, engineering, but potentially also medicine or geology.
Leveraging Terminology Services for FAIR Semantic Data Integration Across NFDI Domains

The National Research Data Infrastructure (NFDI) strives to develop FAIR research data and data services for major scientific disciplines, using terminologies as a key factor for semantic annotations and semantic interoperability of data. Several NFDI consortia provide domain-specific terminologies through Terminology services or registries, offering access, search capabilities, visualization, and downloads. Prioritizing user-friendly access, terminology services seamlessly integrate semantic concepts into applications, often operating in the background to enable smooth semantic annotation and data interoperability. We present exemplary fields of application from selected disciplines and how terminology services support semantic search, user experience, annotation workflows, terminology curation and design.
Pathways Between National and European Research Infrastructures: A Humanities’ Perspective

In this poster we will discuss challenges, requirements and good practices for creating synergies and inter-connections between national and international research and research data infrastructures from the perspective of the humanities. Stakeholders of the NFDI consortium Text+, of the German national node of DARIAH, of the SSH Open Cluster and of related EOSC projects are presenting benefits and outcomes of these structures and their linkages for the actual research data management and for the researchers. How are research data repositories connected and researchers guided to all the various service offers? With an example about the tool registry in the SSH Open Marketplace reused by Text+ and connected to EOSC, we illustrate the overall picture of these infrastructures and their impact in practice.
Toward the Development of NII RDC Application Profile Using Ontology Technology

In recent years, there has been international progress in developing platforms that support the reproducibility and reusability of research data. Typical platforms adopt a service architecture integrating multiple information systems to cover the entire research data lifecycle. In realizing this architecture, specifications for inheriting processes and results executed on different information systems play an essential role. This study introduces our practices for application profile development using ontology technology in the NII Research Data Cloud.
Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI)

The Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) equips social scientists in the Netherlands with the data, tools, and skills that are necessary to answer groundbreaking questions for scientific and policy making purposes. With a variety of use cases to pick from, we aim at engaging in a discussion with other Research data infrastructures to identify synergies but also challenges ahead.
RDMkit: The Research Data Management Toolkit for Life Sciences

Effective data management extends over the entire life cycle of data, from the point of creation through to dissemination and archiving, and usually continues long after a research project is concluded. Tailored guidance for management of research data is increasing its importance among data-driven scientific investigations. There is now increasing demand for Research Data Management (RDM), by funders and host institutions, for researchers to develop and implement data management plans for projects. The RDMkit, by ELIXIR Europe, is an open community-led resource designed to share knowledge on how to carry out RDM in life sciences research. It fills the training and learning gap by providing knowledge on domain-focused RDM considerations, task-focused solutions and resources, and tool assemblies that showcase real-life examples addressing how combinations of tools can be used to go through the data life cycle. The technical infrastructure of the RDMkit is reproducible, making it possible for other organisations to use the structure as a guide and inspiration to set up RDM source for their users. In this talk, RDMkit will be introduced with example best practice guidelines, and integrations and knowledge sources.
RSpace + iRODS

Research infrastructures enabling scalable sharing of data are proliferating, at the institutional, project/consortium and national/international levels. Many of these are domain-specific, but there also is a growing focus on Research Commons that enable general data sharing across domains. Examples include the EUDAT Collaborative Data Infrastructure in Europe, the Gakunin RDM platform in Japan, and the Research Commons planned by Canada’s Digital Research Alliance (DRA). In some cases, such as Gakunin RDM, Research Commons are architected as an integrated set of complementary services, but in most existing and planned Research Commons a disparate set of unconnected services is offered. This paper describes the integration between RSpace, an active content management digital research platform, and iRODS, a policy-driven data management platform, and explains how it is designed to serve as a flexible connecting component that ties together other services that make up a Research Commons. The paper discusses how, by tying together otherwise unconnected services, inclusion of RSpace + iRODS in Research Commons enables streamlined flows of data and metadata between services, enhancing FAIR principles. This will be illustrated by considering inclusion of RSpace + iRODS in the two specific examples of Canada’s proposed Research Commons and the EUDAT Collaborative Data Infrastructure. In both cases the interaction between RSpace, iRODS, and individual services provided by the Commons will be discussed, and a comparison will be made between the two Commons and the benefits derived from the inclusion of RSpace + iRODS.
Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains

FAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset.
Chapter 3. Variations in opening and closing forms in email correspondence in Danish and Swedish

In this study, we investigate how differences and similarities in opening and closing greetings in professional emails in academia manifest themselves in Danish and Swedish. Our data consist of a corpus of 595 emails (primary data) and two surveys with 1836 respondents (supportive data). An important result is that cultural values, e.g., ideals of equality, in both language areas are expressed through similar linguistic means, but that due to different historical developments, the specific encoding is not identical. Another result is that there is a much larger variety in closing greetings than in opening greetings in both languages, which can be derived from the communication situation where openings and closings and the elements within these serve different purposes.
Structure-Driven Representation Learning for Deep Clustering

As an important branch of unsupervised learning methods, clustering makes a wide contribution in the area of data mining. It is well known that capturing the group-discriminative properties of each sample for clustering is crucial. Among them, deep clustering delivers promising results due to the strong representational power of neural networks. However, most of them adopt sample-level learning strategies, and the standalone data point barely captures its holistic cluster’s context and may undergo sub-optimal cluster assignment. To tackle this issue, we propose a Structure-driven Representation Learning (SRL) method by introducing latent structure information into the representation learning process at both the local and global levels. Specifically, a local-structure-driven sample representation strategy is proposed to approximate the estimation of data distribution, which models the neighborhood distribution of samples with potential structure information and exploits statistical dependencies between them to improve cluster consistency. A global-structure-driven cluster representation strategy is designed, where the context of each cluster is sufficiently encoded according to its samples (exemplar-theory) and corresponding prototype (prototype-theory). In this case, each cluster can only be related to its most similar samples, and different clusters are separated as much as possible. These two models are seamlessly combined into a joint optimization problem, which can be efficiently solved. Experiments on six widely-used datasets demonstrate the superiority of SRL over state-of-the-art clustering methods.
Reg-Tune: A Regression-Focused Fine-Tuning Approach for Profiling Low Energy Consumption and Latency

Fine-tuning deep neural networks (DNNs) is pivotal for creating inference modules that can be suitably imported to edge or FPGA (Field Programmable Gate Arrays) platforms. Traditionally, exploration of different parameters throughout the layers of DNNs has been done using grid search and other brute force techniques. Though these methods lead to the optimal choice of network parameters, the search process can be very time-consuming and may not consider deployment constraints across different target platforms. This work addresses this problem by proposing Reg-Tune, a regression-based profiling approach to quickly determine the trend of different metrics in relation to hardware deployment of neural networks on tinyML platforms like FPGAs and edge devices. We start by training a handful of configurations belonging to different combinations of \(\mathcal {NN}\scriptstyle \langle q (quantization),\,s (scaling)\rangle \displaystyle \) or \(\mathcal {NN}\scriptstyle \langle r (resolution),\,s\rangle \displaystyle \) workloads to generate the accuracy values respectively for their corresponding application. Next, we deploy these configurations on the target device to generate energy/latency values. According to our hypothesis, the most energy-efficient configuration suitable for deployment on the target device is a function of the variables q , r , and s . Finally, these trained and deployed configurations and their related results are used as data points for polynomial regression with the variables q , r , and s to realize the trend for accuracy/energy/latency on the target device. Our setup allows us to choose the near-optimal energy-consuming or latency-driven configuration for the desired accuracy from the contour profiles of energy/latency across different tinyML device platforms. To this extent, we demonstrate the profiling process for three different case studies and across two platforms for energy and latency fine-tuning. Our approach results in at least 5.7 × better energy efficiency when compared to recent implementations for human activity recognition on FPGA and 74.6% reduction in latency for semantic segmentation of aerial imagery on edge devices compared to baseline deployments.
Assessing Urdu Language Processing Tools via Statistical and Outlier Detection Methods on Urdu Tweets

Text pre-processing is a crucial step in Natural Language Processing (NLP) applications, particularly for handling informal and noisy content on social media. Word-level tokenization plays a vital role in text pre-processing by removing stop words, filtering irrelevant characters, and retaining relevant tokens. These tokens are essential for constructing meaningful n-grams within advanced NLP frameworks used for data modeling. However, tokenization in low-resource languages like Urdu presents challenges due to language complexity and limited resources. Conventional space-based methods and direct application of language-specific tools often result in erroneous tokens in Urdu Language Processing (ULP). This hinders language models from effectively learning language-specific and domain-specific tokens, leading to sub-optimal results for downstream tasks such as aspect mining, topic modeling, and Named Entity Recognition (NER). To address this issue for Urdu, we have proposed a data pre-processing technique that detects outliers using the Inter-Quartile-Range (IQR) method and proposed normalization algorithms for creating useful lexicons in conjunction with existing technologies. We have collected approximately 50 million Urdu tweets using the Twitter API and conducted the performance analysis of existing language-specific tokenizers (Urduhack and Space-based tokenizer). Dataset variants were created based on the language-specific tokenizers, and we performed statistical analysis tests and visualization techniques to compare tokenization results before and after applying the proposed outlier detection and normalization method. Our findings highlighted the noticeable improvement in token size distributions, handling of informal language tokens, and misspelled and lengthy tokens. The Urduhack tokenizer combined with the proposed outlier detection and normalization yielded tokens with the best-fitted distribution in ULP. Its effectiveness has been evaluated through the task of topic modeling using Non-negative Matrix Factorization (NMF) and Latent Dirichlet allocation (LDA). The results demonstrated new and distinct topics using unigram features while achieving highly coherent topics when utilizing bigram features. For the traditional space-based method, the results consistently demonstrated improved coherence and precision scores. However, the NMF topic modeling with bigram features outperformed LDA topic modeling with bigram features.