Data Management Standards Research Articles

Assessing the FAIRness of Metabolic Bariatric Surgery Registries: a Comparative Analysis of Data Dictionaries from the UK, Germany, France, Netherlands, Norway, and Sweden.

This study is part of an initiative to improve the FAIRness (Findability, Accessibility, Interoperability, Reusability) of metabolic bariatric surgery (MBS) registries globally. It explores the extent to which European registry data can be manually integrated without first making them FAIR and assesses these registries' current level of FAIRness. The findings establish a baseline for evaluation and provide recommendations to enhance MBS data management practices. Data dictionaries from five national MBS registries in Germany, France, the Netherlands, the UK, and a combined registry for Scandinavia (Norway and Sweden) were evaluated regarding their ability to manually integrate registry datasets with one another. The FAIR Data Maturity Model from the Research Data Alliance (RDA) FAIR Data Maturity Model Working Group was used to assess the FAIRness of both metadata and data of the registries. The registries showed significant variability in variables and coding structures, with inconsistent numerical formats and without linkage to international standards such as SNOMED CT, LOINC, or NCIt, making data integration labor-intensive and assumption-heavy. Despite the presence of data dictionaries, all registries failed the FAIR assessment because machine-readable data was unavailable, and only human-readable metadata was available in the form of data dictionaries in a spreadsheet. Our study reveals significant inconsistencies in data structuring and a failure to comply with the FAIR Principles, which limit effective data analysis and comparison. This emphasizes the critical need for standardized data management practices. We recommend four next steps to improve the FAIRness of MBS registries: (1) annotate data elements using standardized terminology systems, (2) deposit registry-level metadata in a repository, (3) request globally unique and persistent identifiers for datasets, and (4) define access restrictions.

General data management workflow to process tabular data in automated and high-throughput heterogeneous catalysis research

Standardized data management is enabled by a Python library to query, merge and process data files and combine them into a dataset with a focus on automated high-throughput heterogeneous catalysis R&D and accessing an electronic laboratory notebook.

Blockchain barriers in hospitals: a stakeholder theoretic perspective

PurposeGrounded in stakeholder theory, this study aims to examine the barriers to blockchain adoption in hospitals in developing countries. It also aimed to explore the interrelationships among these barriers and investigate how the perceptions of clinical and non-clinical professionals in the healthcare industry differ regarding these barriers.Design/methodology/approachThe study context was hospitals in India and proceeded in three phases. First, barriers affecting blockchain application in healthcare were shortlisted using a systematic literature review. In the second phase, a multi-round Delphi study with clinical and non-clinical healthcare experts was conducted to screen and validate the barriers identified in the first phase. Finally, the barriers were ranked and categorized into causal and effect groups using the Grey-DEMATEL technique in the last phase of the study.FindingsThe findings reveal variance in the viewpoints of clinical and non-clinical professionals regarding influential barriers. Overall, the most significant causal barriers were a shortage of IT skills and a lack of standards for patient data management standards in Indian hospitals. Additionally, the study identified the lack of a well-defined strategy for blockchain infrastructure deployment and limited support from hospital management as effect barriers.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to use the integrated Delphi-DEMATEL approach to explore blockchain adoption barriers in hospitals from the expert’s point of view.

Відкрита наука в сучасному дослідницькому просторі

The article comprehensively examines theoretical and practical aspects of open science development in the modern research space. The evolution of the open science concept has been analyzed. It has been determined that the modern understanding of open science is based on the principles of transparency, collaboration, and inclusivity, which are implemented through open access to publications, research data, and methodologies. The role of leading international organizations in the development of open science has been investigated. In particular, SPARC's activities in overcoming inequalities in scientific communication, Science Europe's contribution to the formation of research data management standards, and Coalition S's impact on the transformation of scientific publishing models have been analyzed. It has been established that these organizations play a key role in shaping open science policies, developing infrastructure, and implementing new models of scientific communication. Special attention has been paid to the development of open science in Ukraine in the context of European integration and the formation of national open access infrastructure. The main directions of development have been analyzed, including the creation of a network of institutional repositories, development of open access journals, legislative support, and international cooperation. Ukraine's high performance in open data has been noted, indicating significant progress in implementing public information transparency policies. Based on the analysis, key components of modern open science have been identified, including open research data complying with FAIR principles, open research methods, open peer review of scientific works, and digital infrastructure. The main challenges of open science development have been revealed, particularly financial aspects of open access publishing, technical issues of data standardization and storage, overcoming cultural barriers, and the need to change established practices. The prospects for further development of open science have been substantiated, which are associated with improving technological infrastructure, implementing effective mechanisms for evaluating scientific activities, and creating incentives for adherence to open science principles

Leveraging Machine Learning and Data Mining for Enhanced PEM-Electrolysis Using Standardized Data Management

Green Hydrogen production by water electrolysis is paramount in the transition from fossil fuels towards a carbon neutral industry. Hydrogen is expected to function as energy carrier in mobility, energy storage medium in electricity grids and as regenerative fuel in industrial processes such as steel production. Proton exchange membrane water electrolysis (PEMWE) stands out as a promising technology for hydrogen production due to high current densities with high efficiencies and highly dynamic operation. While the number of publications on the production of PEM electrolysers grows steadily, a lack of standardized methodologies and experimental guidelines persists. Many studies focus on specific components and their manufacturing steps, hindering direct comparison and complicating data interpretation. Consequently, standardization of research data and specification of parameters is the objective of the current research landscape.In this work, we systematically analyzed literature and gathered meta data regarding experimental procedures for lab-scale production of PEM electrolysers. Thereby, special attention is on the identification of potential improvement in data management. The production of electrolysers can be divided into the various tasks as the fabrication of membrane electrode assemblies (MEAs), which includes catalyst ink production, coating and assembly, electrolyser design and characterization. All areas are in turn subdivided into metadata, for example the ionomer to catalyst ratio and catalyst content for the catalyst ink, the coating technology such as spray or slot coating for the coating. In total, over 60 different parameters were considered and a database with over 600 different samples from publications around the world was created. Incomplete datasets were determined by classification using various methods, such as k-nearest-neighbors.The extensive database was implemented in various machine learning algorithms with the goal of identifying parameters with a significant influence on the target variable based on the polarization curve. We then identify correlations between different parameters and formulate guidelines for individual fabrication steps and materials for electrolyzers. An optimization is then applied to determine the most favorable procedure for MEA fabrication and assembling electrolysers, incorporating all the parameters from the database.This work lays the foundation for future publications regarding the standardization of research data and shows how correlations between different parameters can be identified and optimized with the help of data management.

Review of the Seasonal Wastewater Challenges in Baltic Coastal Tourist Areas: Insights from the NURSECOAST-II Project

The NURSECOAST-II project addresses the challenge of managing wastewater in near-coastal tourist destinations around the Baltic Sea, particularly from small treatment plants (<2000 PE) that experience fluctuating flows due to seasonal tourism. These fluctuations make it difficult to meet environmental standards, potentially harming both the environment and tourism. The project has created a GIS-based inventory of small wastewater treatment plants within 100 km of the coast. This inventory includes crucial operational data like flow rates, pollutant levels, and treatment technologies. Initial findings reveal significant discrepancies in data management, regulations, and treatment standards across the Baltic Sea region countries, as EU legislation does not uniformly cover plants under 2000 PE. Key findings highlight that small treatment plants are often undocumented, their environmental impact underestimated, and regulations vary widely. Small plants can significantly contribute to nutrient pollution, affecting the Baltic Sea, particularly in local areas. The data gathered will support local authorities in identifying gaps and improving management strategies. This study stresses the need for harmonized data collection and reporting methods across countries and suggests establishing a unified database accessible to both specialists and the public. The status of the collected data depending on the type of data and country was as follows: 38.11% from Denmark, 46.14% from Estonia, 26.36% from Finland, 15.56% from Germany, 23.47% from Latvia, 34.77% from Lithuania, 14.51% from Poland, and 45.40% from Sweden. Ultimately, this project aims to enhance wastewater management, protect the environment, and improve tourist satisfaction in coastal regions.

Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices.

Expert consensus on core data elements and definitions for adult invasive mechanical ventilation (2024)

Invasive mechanical ventilation stands as a principal therapeutic intervention in the intensive care unit (ICU), yet there exists a lack of standardized data management protocols. To address this, the Chinese Society of Critical Care Medicine organized a working group from disciplines including critical care medicine, respiratory and critical care medicine, respiratory therapy, and health informatics. This group formulated the Expert consensus on core data elements and definitions for adult invasive mechanical ventilation (2024). This consensus established a standardized framework comprising 246 key data elements through a comprehensive literature review. The elements cover patient demographic information, clinical data, ICU admission details, parameters of invasive mechanical ventilation therapy, vital signs, and blood gas analysis, aiming to standardize data management in both clinical practice and research. Moreover, the consensus accentuates the necessity for continuous updates to the data element standards in line with advancements in mechanical ventilation technology, ensuring that they reflect the latest clinical practices and technological progress.

MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the gap between benchmarking, experimentation, and optimization of software systems. It allows users to create one-click benchmarking and experimentation scenarios for multi-VM setups in the cloud with optional standard and custom metrics collection and data management of the results. MLOS provides a collection of pluggable optimizers (ML or otherwise) for efficiently exploring the configuration space and finding optimal values for parameters across the entire software stack, including VM, OS kernel, and userland applications. It has a convenient lightweight interface for data storage, access, and visualization for a user-friendly notebook experience. These features make it a useful platform for both systems developers and auto-tuning researchers. MLOS is an active open-source project and is being used within Azure Data. A video demonstrating MLOS is available at https://aka.ms/MLOS/VLDB-2024-demo-video.

Need for Data Standardization and Infrastructure of Research Data Management to Promote Using Real-world Data

Real-world data (RWD) have been increasingly used for regulatory decision-making and as a control group for new drug approval applications. RWD is also helpful in understanding information such as risk factors (e.g., pre-existing medical conditions, personal protective equipment, travel, contacts, smoking, and exposure to animals) and vaccination status for the coronavirus disease 2019 (COVID-19). The methodology of utilizing RWD is inconsistent across healthcare institutions. However, there are possible solutions to standardize RWD for clinical data use, which include the use of Clinical Data Interchange Standards Consortium (CDISC) standards, tools, and concepts. This study examines the availability of CDISC and other international standards for the utilization of RWD with concrete examples and presents the potential platform for implementation. We consider the solution currently available to temporarily convert clinical data-warehouse (DWH) data into the Fast Healthcare Interoperability Resources (FHIR) format to comply with the CDISC standard. This approach would allow for converting institution-level standards to national standards as an interim solution until FHIR is supported, mapping national standards to international standards. We believe that the ideal research environment is a data platform that complies with national and international regulations related to RWD applications. Within such a platform, users can share data freely, rather than rely on a specific facility or vendor. Data platform developments are progressing in Japan and globally. In Japan, initiatives to use research data on research data platforms are being conducted. We are experimenting with implementing tools and knowledge shared by CDISC.

Evaluating the influencing factors and effects of the digitalization of management control

Purpose This study aims to investigate the influences on the digitalization of management control and the effects of this digitalization on management control tasks performed, management control instruments used and the organization of management control. Design/methodology/approach This empirical analysis is based on a survey of 189 management accountants and managers responsible for management control in Germany, Austria and Switzerland. Logistic regression analyses were used to test the investigated effects. Findings This study finds that digital competencies, standardization of processes and data management contribute to the digitalization of management control. This study further finds that digitalization significantly increases the coverage of strategic and operational management control tasks and the use of operational management control instruments but not of strategic instruments. Originality This study investigates the influence of digitalization in management control on strategic and operational management control tasks, instruments and organizations. In contrast, prior research has focused on single aspects of management control or analyzed the impact on the entire company. This is also the first study, to the best of the authors’ knowledge, to systematically identify potential influences on the digitalization of management control and analyze them empirically.

Standardisierter Datenmanagementplan für die Bildungsforschung (Stamp) – Ein projektbegleitendes Tool für das Forschungsdatenmanagement

ZusammenfassungDie empirische Bildungsforschung zieht ihre Erkenntnisse aus der Erhebung und Auswertung von Daten. Einheitliche Standards sind notwendig, um den steigenden Anforderungen an das Forschungsdatenmanagement im Kontext einer zunehmenden Kultur des Datenteilens zu begegnen. Das Verbundprojekt Domain-Data-Protokolle für die empirische Bildungsforschung (DDP-Bildung) entwickelte hierzu den Stamp (Standardisierter Datenmanagementplan für die Bildungsforschung). Der Stamp basiert auf dem „Domain Data Protocol“-Konzept von Science Europe und dient der Sicherung der Datenqualität und der Nachnutzbarkeit von Forschungsdaten. In die Entwicklung des Stamps flossen Expertisen aus der Forschungsdateninfrastruktur sowie von der Scientific Community der Bildungsforschung geäußerte Bedarfe ein. Ein zentraler Unterschied zu klassischen Datenmanagementplänen besteht darin, dass der Stamp Informationsquellen verknüpft und fachspezifische Vorgehensweisen in Form von Praxisbeispielen unter Beachtung rechtlicher Vorgaben illustriert. Hierbei begleitet der Stamp wissenschaftliche Projekte von der Planungsphase (inklusive Projektanträgen) über die Erhebung und bis zur Archivierung der Daten. Die vorliegende Arbeit beschreibt den Entwicklungsprozess des Stamps und stellt dessen Bestandteile vor.

A Comparative Study of Deep-Learning Autoencoders (DLAEs) for Vibration Anomaly Detection in Manufacturing Equipment

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.

Abstract 3555: COSMIC: two decades of curating somatic variants in cancer

Abstract In 2004, COSMIC was one of the first initiatives to integrate global data on somatic mutations in cancer. At the time it was explicit that the fragmentation of genetic datasets was a major obstacle to understand the processes driving cancer. A team of expert curators and bioinformaticians was tasked with identifying and cataloguing data related to somatic variants, as well as relevant demographic, clinical, and patient information from published studies and making these data easily accessible to the research community. Over the last two decades we have witnessed the incredible progress in cancer genomics enabling whole genome studies, resulting in the exponential growth of data generated by cancer research. During its first year, COSMIC integrated data from 1672 scientific papers, cataloguing 1755 unique mutations across 21 genes. At present, twenty years later it is common for a single publication to describe tens of thousands of genome-wide mutations and now COSMIC includes 24 million genomic variants collected from more than 1.5 million patient samples acquired from over 29,000 scientific publications. Today, an important part of COSMIC's mission is to help translate this information for improving cancer treatment and patient care. To achieve this, the main catalogue of somatic mutations is supported by 6 accompanying resources that focus on different aspects of molecular oncology. The Cancer Gene Census and Cancer Mutation Census describe the roles of genes and mutations in oncogenesis, based on literature curation and analysis of the core mutation catalogue. COSMIC 3-D visualises mutation frequency in the context of the protein structure, and Mutational Signatures catalogues the mutagenic processes behind the nature of mutations at the genome level. To help target molecular alterations in clinical practice, Actionability and the catalogue of mutations causing drug resistance inform the availability of therapeutic options for cancer and how their efficacy is influenced by the genetic profile of the cancer. The ongoing development of sequencing techniques and new diagnostic methods as well as computational approaches, including deep learning and use of generative AI, give us hope that the next 20 years will be equally revolutionary for data-driven oncology. However, the abundance of new sources of diverse datasets makes data fragmentation an evolving challenge for the whole research community. Developing and adopting common standards for data formats, management and usage will be critical to assure inclusive, efficient, and effective translation of genomic research into a clinical practice. Citation Format: Zbyslaw Sondka, Madiha Ahmed, Joanna Argasinska, David Beare, Nidhi Bindal Dhir, Denise Carvalho-Silva, Manpreet Singh Chawla, Stephen Duke, Ilaria Fasanella, Muhammed Fouzan, Avirup Guha Neogi, Susan Haller, Bhavana Harsha, Balazs Hetenyi, Leonie Hodges, Alex Holmes, Steven Jupe, Rachel Lyne, Madhumita Madhumita, Thomas Maurel, Karen McLaren, Sumodh Nair, Helder Pedro, Amaia Sangrador-Vegas, Helen Schuilenburg, Zoe Sheard, Michael Starkey, Rebecca Steele, Sari Ward, Jennifer Wilding, Siew Yit Yong, Jon Teague. COSMIC: two decades of curating somatic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3555.

Abstract 7432: Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal

Abstract The Computational Resources for Cancer Research website is a public portal supported by NCI that democratizes access to available computational resources for the cancer research community. In supporting an emerging FAIR (Findable, Accessible, Interoperable, Reusable) data science ecosystem inclusive of software, models and data, the goals of the portal are to serve cancer data scientists by increasing understanding, access, and adoption of computational resources for cancer research, and to foster new collaborative research projects. Initially populated with resources developed in NCI supported collaborations with the US Department of Energy, the portal is serving as a unified reference and resource for computational resources developed broadly by the cancer community. These collaborative projects include CANDLE (the Cancer Distributed Learning Environment), IMPROVE (Innovative Methods and data for PRedictive Oncology model Validation and Evaluation), ADMIRRAL (AI Driven Multi-scale Investigation of Ras-Raf Activation Lifecycle), MOSSAIC (Model Outcomes using Surveillance Data and Scalable AI for Cancer) and ATOM (Accelerating Therapeutics for Opportunities in Medicine). The resources include software, models, and datasets as well as descriptive use cases and references to tutorials associated with many of the described resources. The portal also integrates content from the Predictive Oncology Model and Data Clearinghouse (modac.cancer.gov) which can be accessed through REST APIs. Ongoing development of the portal is driven by community insights and contributions including input from an interdisciplinary advisory committee, working groups, early users, and preliminary feedback received at AACR 2023. The portal will also support community contributions of computational resources. In addition, the portal highlights computational approaches in emerging and challenging areas, such as digital twins for cancer. The portal will enable the cancer research community to learn about new computational research methods through use cases and educational material, access cancer related software, datasets, and AI models, engage with peers and cross-disciplinary researchers at different career levels through an interactive forum, explore emerging areas, such as computational imaging, community-based standards for data management, predictive radiation oncology, and digital twin technologies, learn about in industry and community events, conferences, and workshops, and create new collaborative research projects. The presentation will deliver insights into key resources offered through the Computational Resources for Cancer Research Portal, how the portal will help the cancer research community identify and adopt computational resources of interest, and how researchers can join and participate in this growing community. Citation Format: Eric Stahlberg, Lynn Borkon, Ruth Frost, Sara Jones, Amar Khalsa, Lauren Lewis, Emily Greenspan, Sunita Menon. Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7432.

Research data management in selected East African libraries: A survey

This article investigates the knowledge of research data management and services among library professionals in selected East African libraries. A survey research design was employed, and data was collected using a structured questionnaire from 180 respondents representing four East African countries: Malawi, Mozambique, Zambia and Zimbabwe. The findings reveal that only 31.1% of the selected East African librarians agreed that their institutional libraries provided research data management services. The standard research data management services offered by their libraries included data publishing, sharing and reuse, while collaboration with academic programmes was identified as an essential approach for research data management skill development. The study highlights the need for librarians to acquire legal, policy and advisory skills and knowledge of institutional and extra-institutional resources and the research life cycle for effective research data management service delivery.

FAIRness in digital forensics datasets’ metadata – and how to improve it

The availability of research data (datasets) and compliance with FAIR principles—Findability, Accessibility, Interoperability, and Reusability—is critical to progressing digital forensics. This study evaluates metadata completeness and assesses the alignment with the FAIR principles using all 212 datasets from NIST's Computer Forensic Reference DataSet Portal (CFReDS). The findings underscore deficiencies in metadata quality and FAIR compliance, emphasizing the need for improved data management standards. Based on our critical review, we then propose and discuss various approaches to improve the status quo.

Revolutionizing Data Management through Cloud-Based Data Fusion Platforms in Distributed Network Architectures

Data management is developing rapidly, and we need solutions that can handle massive volumes of diverse data. Especially for cloud-based data fusion and global network designs. Our research offers a fresh solution. Each difficult formula in this manner improves the system. Standardizing, matching, translating, and merging data from several sources is the fundamental strategy for data integration and management. We found that this alternative is superior to standard data management systems for growing, working fast, consistently, securely, and accurately integrating data, as well as cost-effectiveness. Data's visual presentation enhances the method's advantages and shows its potential. This research proves the technique works and illustrates how it may be utilized to advance the field. Supporting today's sophisticated data systems is a major advance. It's a solid, scalable data management solution that can evolve.

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.

Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. 5 Laryngoscope, 134:1333-1339, 2024.

Exploring and Improving Workflows for the Donation and Curation of Research Data

Managing research data and preparing it for long term archiving at a research data center is a time-consuming, repetitive and error prone process without obvious rewards or incentives. To address these challenges, we aim at the integration of a standard data management tool into one of the research data centers of the German Network of Educational Research Data. The goal is to improve workflows for both, data donors and curators and to explore transferable solutions that allow researchers and curators to work on the same platform when entering and editing metadata.