Reviewerly/exHarmony huggingface dataset:
Stamp - Standardized Data Management Plan for Educational Research

While there is a strong tendency towards a harmonized, cross-disciplinary research data management (RDM) (Netscher et al., 2022), researchers require more guidelines and examples, tailored to their research discipline (Grootveld et al., 2018). Therefore, Science Europe (2018: 9) proposes developing so-called domain data protocols (DDP), “a ‘model DMP’ for a given domain or community”. Based on this concept, the project Domain Data Protocols for Educational Research[1] designed the Stamp - Standardized Data Management Plan for Educational Research (DDP-Bildung and German Network of Educational Research Data, 2023). Although the Stamp was designed to support researchers in educational research, we expect that RDM is rather a matter of the data processed, the methods employed, and the content of data, than of a particular research discipline or community, such as educational research. To discuss this expectation and the usability of the Stamp outside educational research, we organized various workshops with representatives from other research disciplines. In our talk at the CoRDI 2023, we will introduce the Stamp, recap findings of two of the workshops, introduce the next steps to examine the useability of the Stamp outside educational research, and draw some conclusions on how to adapt the Stamp to other disciplines and how it fosters a harmonized, cross-disciplinary RDM. [1] The project DDP-Bildung was funded by the German Federal Ministry of Education and Research (grant number: 16QK01).
Galaxy and RDM

The increasing amount of data generated by scientific research poses the challenge of providing an adequate infrastructure and tools that facilitate FAIR (Findable, Accessible, Interoperable and Reusable) data access, manipulation, analysis and visualization. Often, the burden of managing the metadata associated with the original data and the analysis lies with the researchers. The open source Galaxy platform [1] is well-known for supplying tools and workflows for reproducible and transparent data analysis across scientific disciplines. It is a multi-user environment which facilitates sharing of e.g. tools, workflows, notebooks, visualizations, and data with others. There are three large Galaxy instances (US, Europe [2] and Australia) used by hundreds of thousands of researchers worldwide and that are using PBs of data. Galaxy handles the metadata transparently, releasing scientists from the burden and making it less prone to human errors. These features can be used without technical background by using a web browser or, for experts, through the Galaxy API.
RDM in Chemistry: How to Educate and Train Future Researchers to Manage Their Data

For in-depth research data management in chemistry, a cultural change is inevitable. To foster this change, future researchers need to be educated accordingly. The presentation will provide an overview of the first teaching approaches in student courses in chemistry at RWTH Aachen University. On the long range, the integration into curricular teaching is key to the cultural change.
Data Steward Service Center (DSSC): FAIRagro RDM-Expertise Hub

The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.
MetaBelgica Project

Trustworthy metadata about entities related to cultural heritage is important to correctly identify bibliographic meta-data, attribute works, identify works of public domain (based on contributors’ date of death), or to support Named Entity Linking (NEL) for digitised documents. However, in Belgium such data is currently dispersed between numerous institutions, modelled with different ontologies, represented in different formats and curated in different languages. GLAM institutions would profit from data about Belgian entities to correctly annotate their collections.Furthermore, this would facilitate research in Belgium and abroad. The current situation does not only complicate data exchange in a national and international setting, but also leads to duplicate data curation efforts and data quality issues, negatively impacting the users’ experience. This paper introduces the MetaBelgica project, coordinated by KBR - The Royal Library of Belgium with the Royal Museums of Fine Arts, the Royal institute for Cultural Heritage, and the Royal Museum of Art and History, to improve the status quo. The aforementioned Federal Scientific Institutes (FSIs), belonging to the Belgian Science Policy Office (BELSPO), joined forces to develop a shared Linked Data platform for managing shared entities. This platform, based on Wikibase (the technology behind Wikidata), aims to ensure FAIR data about persons, organisations, time/events and locations related to Belgian cultural heritage. We will integrate data about millions of Belgian entities from the four participating FSIs into a Wikibase instance and make it accessible by using Persistent Identifiers. This will not only professionalize our own data management and improve data quality, but according to received project support letters, also impact other regional, national and European institutions.
Ten Simple Rules for Designing and Building a FAIR Research Infrastructure

One of the key priorities of The European Strategy Forum on Research Infrastructures (ESFRI) is to build sustainable and FAIR (Findable, Accessible, Interoperable, Reusable) infrastructures. However, designing and building such infrastructures requires careful consideration of various factors, such as data interoperability, operational sustainability, and governance. This poster proposes ten simple rules, inspired by Ten Simple Rules for scientific research, for designing and building a research infrastructure drawing from existing initiatives particularly from experiences in preparation of DiSSCo (Distributed System of Scientific Collections) – a new research infrastructure that was in the ESFRI 2018 roadmap. While these rules are not comprehensive, they highlight a few essential traits that can be applied across different disciplines. For each rule, we highlight how within DiSSCo we accomplished the specific aspect.
BAM Data Store

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.
A Comparative Study on the Design of Reading Teaching Activities for Novice and Experienced Primary School English Teachers: Based on the “View of English Learning Activities”

From the perspective of English learning activities, based on the three dimensions of the classification framework: learning and understanding activities, application and practice activities, transfer and innovation activities, this paper compares and analyzes the design of teaching activities in reading class between a novice teacher and an experienced teacher. Based on research results, this paper puts forward the following suggestions: 1. Teachers should pay attention to different types of activities and balance the proportion of different activities. 2. Teachers should focus on the use of transfer and innovation activities, and pay attention to the cultivation of students’ thinking quality. 3. Novice teachers should learn more from experienced teachers and attach importance to the connection and progression of activities.
Kartun dan Komik Strip untuk Mengajar Bahasa dan Kepekaan Lintas Budaya Mahasiswa BIPA dari Thailand

The use of cartoons and comic strips in cross-cultural and language instruction has significant advantages for Thai students' learning abilities. This study aims to: (1) determine Thai students at Muhamadiyah University in Jember's level of Indonesian comprehension through the use of cartoon and comic strip learning materials; ( 2) Using cartoons and comic strips as educational tools to introduce Thai students at Muhamadiyah University of Jember to Indonesian culture; 3) Provide a description of the cartoon and comic strip formats utilized in this study; also (4) Portray the aftereffects of further developing language picking up utilizing animation and funny cartoon gaining media for understudies from Thailand at the Muhamadiyah College of Jember. Descriptive statistics and qualitative descriptive analysis methods were used to analyze the data. The study's findings assign a BIPA level to Thai students enrolled at Muhammadiyah University of Jember. Additionally, the trial's findings demonstrate a rise in Indonesian language proficiency and cross-cultural awareness.
ChainKeeper: A cross‐chain scheme for governing the chain by chain

Abstract With the rapid application of consortium chains, supervising these systems has become a challenge for governments. The centralized model fails to deliver supervision services that are both open and transparent. Given the benefits of decentralization, non‐tampering, and traceability offered by blockchains, researchers propose the concept of ‘governing the chain by chain’, which involves supervising multiple consortium chains by constructing a blockchain. Under this idea, the cross‐chain scheme becomes the key to achieving excellent supervision. Existing studies have shortcomings and cannot meet the requirements of universality, security, and efficiency in cross‐chain supervision scenarios. Aiming at the challenges, we propose ChainKeeper, a cross‐chain scheme for governing the chain by chain. The innovation of our work lies in three points. First, a modular node proxy program is designed to adapt to various implementations of consortium chains. Second, a verifiable node random selection method is put forward to improve the throughput of cross‐chain data transmission. Finally, a verifiable identity threshold signature method is proposed to prevent the cheating behavior of malicious nodes. To verify the universality of ChainKeeper, we built a prototype system on three types of consortium chains. The experimental results show that ChainKeeper can achieve high throughput, outperforming two state‐of‐the‐art cross‐chain schemes.
Exploring How AI Answering Models Understand and Respond in Context.

Abstract: Question answering (QA) is an important capability for artificial intelligence systems to assist humans by providing relevant information. In recent years, large pretrained language models like BERT and GPT have shown promising results on QA tasks. This paper explores how two state-of-the-art models, BERT and GPT-4, understand questions and generate answers in conversational contexts. We first provide an overview of the architectures and pretrained objectives of both models. Then we conduct experiments on two QA datasets to evaluate each model's ability to reason about questions, leverage context and background knowledge, and provide natural and logically consistent responses. Quantitative results reveal the strengths and weaknesses of each model, with BERT demonstrating stronger reasoning abilities but GPT-4 generating more human-like responses. Through qualitative error analysis, we identify cases where each model fails and propose explanations grounded in their underlying architectures and pretraining approaches. This analysis provides insights into the current capabilities and limitations of large pretrained models for open-domain conversational QA. The results suggest directions for improving both types of models, including combining their complementary strengths, increasing reasoning ability, and incorporating more conversational context. This work highlights important considerations in developing AI systems that can intelligently understand and respond to natural language questions.
Mining Roles Based on User Dynamic Operation Logs

Background: If we rely solely on whether to assign permissions together to determine roles, the roles we generate may not necessarily reflect the needs of the system. Therefore, the role generation process can be done based on user-to-permission dynamic relationships, such as user dynamic operation logs, thus providing the motivation for this work. Methods: In our paper, we introduce a special generalization process and a frequent set-based analysis method to generate roles based on the particular data type of user dynamic operation logs so that the time factor of permissions used is considered before the process of role generation to generate the roles such also as auth_perms(r)={p_1,p_2,p_3}. Results: Our algorithm is less time consuming and generates less roles than traditional algorithm. Furthermore, the roles generated by the algorithm can better describe the real needs of the system and have better interpretability. Conclusion: The results show that the algorithm has superior performance and useful role generation compared to traditional algorithm.
Pelatihan Pembuatan Media Pembelajaran bagi Guru SD di Kecamatan Keritang

The aim of this devotion is to help Elementary School Teachers in desaining and making interesting learning media tomake teaching and learning process run well because the students have motivation to follow the lteaching and learning process at the class. But in reality, many teachers did not use media whenthey teach their students. Some teachers still use media in teaching process but only fictures and multipurposes media.because of pictures is always used by the teachers it makes the media is not interesting for the students and they do not have high motivation to follow the teaching and learning process. To solve the problem, the writers do devotion for the Elementary schools in desaigning and making learnng media to get students’ interesting teaching and learning process. After getting this workshop the teachers can make and use some teaching and learning media like slide power point witch use teachers’ picture and sound to make clear material.
Pelatihan Manajemen Arsip Digital berbasis Google Drive Desktop bagi Pengurus Pondok Pesantren se-Kecamatan Sangkapura

The training on digital archive management based on Google Drive Desktop was conducted with the aim of enhancing the capability of Islamic boarding school administrators in managing digital archives. The training took place on Sunday, June 18, 2023, at the MA Umar Mas'ud Sangkapura Auditorium, following prior coordination and distribution of invitation letters to the administrators of Islamic boarding schools in the Sangkapura Sub-District. The participants consisted of 29 administrators from 15 Islamic boarding schools in the Sangkapura Sub-District. The training covered the fundamental concepts of digital archive management, the use of Google Drive Desktop, best practices in digital archive management, and folder and file organization. The training employed presentation sessions, interactive discussions, and hands-on practice using Google Drive Desktop. Evaluation was conducted through pre- and post-training surveys to assess the participants' understanding and skills. Additionally, a satisfaction survey was conducted to evaluate the quality of the training. The evaluation results indicated a significant improvement in the participants' understanding and skills in digital archive management after the training. The satisfaction survey revealed that the majority of the participants were highly satisfied with the training material, delivery methods, clarity of instructions, and overall training experience. It is anticipated that this training will have a positive impact on digital archive management in Islamic boarding schools across the Sangkapura Sub-District and serve as a foundation for the development of similar training programs in other locations.
Design of an Application to Calculate Student Grades in Learning Logic Informatics Propositional Calculus Material

Propositional calculus is a method used to calculate the truth value of a proposition. Propositional calculus is commonly studied by various students, from high school to college. Propositional calculus studies the truth value (True/False) of a proposition. The proposition is then processed into the steps of working on the proposition according to the order of operator priority, which is packaged into truth tables and logical expressions. The proposition work done is not easy, but consumes a lot of time and writing media if done manually. Errors in doing proposition work result in inaccurate truth values obtained. Therefore, the author is interested in making an application to calculate student grades using the propositional calculus method that can be used to overcome the problem of working on propositions manually, so that it can produce accurate truth values, which can also be used as a learning medium and media for students to calculate and see the results of their grades manually.
Movement computation of an articulated body using coordinate transformation

Abstract In this paper, we propose a new formula for calculating the motion of an articulated rigid body using images obtained with a moving camera with an autofocus ability. Each of the input image scenes consists of moving joints with their own position and orientation unknown. Based on the computed coordinate system of the previous joint, the triangulation method finds the position of the feature point, and the computed feature point establishes the coordinate system for the next adjacent joint. First, the external and internal parameters of a camera are calculated using the optimization technique using the feature points in fixed environmental objects. The first joint is defined as the joint in contact with the environment. Based on the first joint, the movements of subsequent joints are computed in order. Afterwards, we calculate the motion for the connected joint with respect to the previous joint in a tree structure. To verify the accuracy of the movement of a model, a three-dimensional reconstruction was performed by using computed joint movements. The proposed formula is verified by the result of 3D reconstruction.
Réseaux adversaires génératifs à croissance progressive utilisant le rapport de conditionnement pour la modélisation des faciès dans les aquifères complexes

Groundwater-flow and contaminant-transport modeling rely on methods of converting a set of field observations into geologic models that represent the subsurface structure. These geologic models also must replicate important geologic features such as connectivity. Recently, researchers have begun to use machine learning methods such as generative adversarial networks (GANs). This study focuses on a progressive growing GAN (PGGAN) to condition on measured data. Given a latent variable and an array that provides field observations, the generators of the conditioned PGGAN are tasked to produce geologically realistic images of channel aquifers that match field observations. Although largely successful, the conditioning behavior of these networks still has some issues, and how the model performs the conditioning task across its layers is not yet fully understood. To better understand this conditioning mechanism, the behavior of these networks was measured using the conditioning ratio, which is a novel metric that determines the magnitude of the influence of the conditioning data. The conditioning ratio was measured across multiple layers within the generator during training, as well as with various modifications to the network architecture. The results revealed two distinct conditioning behaviors that are based on the number of conditioning arrays injected into the generator. Results also showed that decreasing the starting resolution for the generator can slow down the learning process. Overall, the numerical experiments prove the value of measuring the conditioning ratio of layers within the generator. These approaches can be used as diagnostic tools to assist in the design of future PGGAN architectures.
Universal structural patterns in sparse recurrent neural networks

Abstract Sparse neural networks can achieve performance comparable to fully connected networks but need less energy and memory, showing great promise for deploying artificial intelligence in resource-limited devices. While significant progress has been made in recent years in developing approaches to sparsify neural networks, artificial neural networks are notorious as black boxes, and it remains an open question whether well-performing neural networks have common structural features. Here, we analyze the evolution of recurrent neural networks (RNNs) trained by different sparsification strategies and for different tasks, and explore the topological regularities of these sparsified networks. We find that the optimized sparse topologies share a universal pattern of signed motifs, RNNs evolve towards structurally balanced configurations during sparsification, and structural balance can improve the performance of sparse RNNs in a variety of tasks. Such structural balance patterns also emerge in other state-of-the-art models, including neural ordinary differential equation networks and continuous-time RNNs. Taken together, our findings not only reveal universal structural features accompanying optimized network sparsification but also offer an avenue for optimal architecture searching.