Data Science Journal最新文献

{"title":"Open Science, Health Data and Epistemic Harms: A Multidisciplinary Reflection.","authors":"Tatenda Chatikobo, Frances Griffiths, Nikita Hayden, Gary Leeming, Ankita Mishra, Eva Morris, Luca Schirru, Nathanael Sheehan, Andrew Williams, Sharifah Sekalala","doi":"10.5334/dsj-2026-015","DOIUrl":"10.5334/dsj-2026-015","url":null,"abstract":"<p><p>Open Science (OS) promises to democratise knowledge and reduce epistemic inequalities. However, a critical analysis reveals the potential of OS to amplify structural vulnerabilities, especially for people and communities already at the margins. With a particular focus on health data, this interdisciplinary essay examines how OS infrastructures perpetuate epistemic harms through the dominance of Eurocentric knowledge norms, legal regimes and corporate capture. Amidst the rapidly evolving health and data landscape, realising the social justice potential of OS, especially in healthcare, demands moving beyond techno-optimism to approaches that centre plural epistemologies, relational accountability and community empowerment.</p>","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"25 ","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7619076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information Needs and Data Harmonization-Two Sides of the Same Coin?","authors":"A J Million, Jenny Bossaller, Sanja Gidakovic","doi":"10.5334/dsj-2025-030","DOIUrl":"10.5334/dsj-2025-030","url":null,"abstract":"<p><p>This article reports on a quality improvement project aimed at supporting the Community Firearm Violence Prevention (CFVP) network at the University of Michigan in the U.S. We examine the information and data needs of researchers in public health, medicine, and criminal justice. Semi-structured interviews with CFVP affiliates and researchers confirm the existence of significant limitations in existing datasets and resources to conduct firearm-violence prevention research. Study participants discussed (1) the infrastructure and research support that would improve their productivity; (2) benefits of using and combining datasets, (3) challenges when accessing data, and (4) data collection and harmonization strategies that increase research impact. We present findings highlighting data-related challenges in this space that concern researchers, data scientists, and policymakers. We conclude that researchers' information needs and data harmonization initiatives are interrelated phenomena. Our observation carries implications for large-scale data harmonization efforts in the social and behavioral sciences.</p>","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"24 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data on the Margins – Data from LGBTIQ+ Populations in European Social Science Data Archives","authors":"Jonas Recker, Anja Perry","doi":"10.5334/dsj-2024-039","DOIUrl":"https://doi.org/10.5334/dsj-2024-039","url":null,"abstract":"Data gaps are a significant lack of data about marginalized groups existing due to unequal power relations (D’Ignazio and Klein, 2020). They both perpetuate and result in a dominance of male, white, hetero, and cis perspectives in how we make sense of and interact with the world. The most prominent data gap is the gender data gap notably described by Criado-Perez (2020). However, not only women, but all marginalized groups are affected by such gaps, as data about them are frequently not collected due to a disregard on behalf of those in power of the need to do so. LGBTIQ+ people, considered a ‘hidden population’ by demographers, are a case in point. The acronym is used to refer to lesbian, gay, bisexual, trans, intersex, and queer people, as well as all people with non-normative sexual or gender identities, including asexual and agender people, who do not consider themselves as falling under one of these labels.\u0000A first step towards identifying and closing data gaps is to take stock of data that already exist. In this paper we give an overview of LGBTIQ+ data in European social science archives. We researched all data archives of CESSDA ERIC, the Consortium of European Social Science Data Archives, and found 66 LGBTIQ+ datasets in 9 of the 34 member and associated archives and 1 former member archive. We discuss characteristics, coverages, and findability of the identified datasets and approach the question of potential data gaps by analyzing the keywords assigned to each dataset by the archive.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"16 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights on Sustainability of Earth Science Data Infrastructure Projects","authors":"A. Virapongse, James Gallagher, B. Tikoff","doi":"10.5334/dsj-2024-014","DOIUrl":"https://doi.org/10.5334/dsj-2024-014","url":null,"abstract":"We studied 11 long-term data infrastructure projects, most of which focused on the Earth Sciences, to understand characteristics that contributed to their project sustainability. Among our sample group, we noted the existence of three different types of project groupings: Database, Framework, and Middleware. Most efforts started as federally funded research projects, and our results show that nearly all became organizations in order to become sustainable. Projects were often funded for short time scales but had the long-term burden of sustaining and supporting open science, interoperability, and community building–activities that are difficult to fund directly. This transition from ‘project’ to ‘organization’ was challenging for most efforts, especially in regard to leadership change and funding issues.\u0000Some common approaches to sustainability were identified within each project grouping. Framework and Database projects both relied heavily on the commitment to, and contribution from, a disciplinary community. Framework projects often used bottom-up governance approaches to maintain the active participation and interest of their community. Database projects succeeded when they were able to position themselves as part of the core workflow for disciplinary-specific scientific research. Middleware projects borrowed heavily from sustainability models used by software companies, while maintaining strong scientific partnerships. Cyberinfrastructure for science requires considerable resources to develop and sustain itself, and much of these resources are provided through in-kind support from academics, researchers, and their institutes. It is imperative that more work is done to find appropriate models that help sustain key data infrastructure for Earth Science over the long-term.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"140 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using OpenBIS as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the CRC 1411 Design of Particulate Products","authors":"Fabian Plass, S. Englisch, B. Apeleo Zubiri, Lukas Pflug, E. Spiecker, Michael Stingl","doi":"10.5334/dsj-2023-044","DOIUrl":"https://doi.org/10.5334/dsj-2023-044","url":null,"abstract":"The digital transformation and consequent use of new digital technologies not only have a substantial impact on society and companies, but also on science. Analog documentation as we have known it for centuries will eventually be replaced by intelligent and FAIR (Findable, Accessible, Interoperable, and Reusable) systems. In addition to the actual research data and results, metadata now plays an important role not only for individual, independently existing projects, but for future scientific use and interdisciplinary research groups and disciplines as well. The solution presented here, consisting of an electronic laboratory notebook and laboratory information management system (ELN-LIMS) based on the openBIS (open Biology Information System) environment, offers interesting features and advantages, especially for interdisciplinary work. The Collaborative Research Centre (CRC) 1411 ‘Design of Particulate Products’ of the German Research Foundation is characterized by the cooperation of different working groups of synthesis, characterization, and simulation, and therefore serves as a model environment to present the implementation of openBIS. OpenBIS, as an open source ELN-LIMS solution following FAIR principles, provides a common set of general entries with the possibility of sharing and linking (meta-)data to improve the scientific exchange between all users.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"87 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness","authors":"Romain David, Audrey S. Richard, Claire Connellan, Katharina B. Lauer, Maria Luisa Chiusano, Carole Goble, Martin Houde, Isabel Kemmer, Antje Keppler, Philippe Lieutaud, Christian Ohmann, Maria Panagiotopoulou, Sara Raza Khan, Arina Rybina, Stian Soiland-Reyes, Charlotte Wit, Rudolf Wittner, Rafael Andrade Buono, Sarah Arnaud Marsh, Pauline Audergon, Dylan Bonfils, Jose-Maria Carazo, Remi Charrel, Frederik Coppens, Wolfgang Fecke, Claudia Filippone, Eva Garcia Alvarez, Sheraz Gul, Henning Hermjakob, Katja Herzog, Petr Holub, Lukasz Kozera, Allyson L. Lister, José López-Coronado, Bénédicte Madon, Kurt Majcen, William Martin, Wolfgang Müller, Elli Papadopoulou, Christine M.A. Prat, Paolo Romano, Susanna-Assunta Sansone, Gary Saunders, Niklas Blomberg, Jonathan Ewbank","doi":"10.5334/dsj-2023-035","DOIUrl":"https://doi.org/10.5334/dsj-2023-035","url":null,"abstract":"The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135153534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Launch of the &lt;em&gt;Data Science Journal&lt;/em&gt;&amp;nbsp;in 2002","authors":"Francis J. Smith","doi":"10.5334/dsj-2023-011","DOIUrl":"https://doi.org/10.5334/dsj-2023-011","url":null,"abstract":"The Committee on Data for Science and Technology of the International Council of Scientific Unions (CODATA) decided in February, 2001 to publish a new on-line peer reviewed journal, freely available to all. It would be called the Data Science Journal. The subsequent steps taken to launch this journal are described, leading to the publication of the first issue of 10 papers in April 2002 and of the first volume of 19 papers by December 2002. Some necessary corrections and improvements before the publication of a second volume of 19 papers in 2003 are also described.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135140985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making Data Management Plans Machine Actionable: Templates and Tools","authors":"J. Philipson, A. Hasan, H. Moa","doi":"10.5334/dsj-2023-029","DOIUrl":"https://doi.org/10.5334/dsj-2023-029","url":null,"abstract":"Since September of 2019, a task group within the European Open Science Cloud - EOSC Nordic Project, work-package 5 (T5.3.2), has focused its attention on machine-actionable Data Management Plans (maDMPs). A delivery working-paper from the group ( Hasan et al. 2021) concluded in summary that extracting useful information from traditional free-text based DMPs is problematic. While maDMPs are generally more FAIR compliant, and as such accessible to both humans and machines, more interoperable with other systems, and serving different stakeholders for processing, sharing, evaluation and reuse . Different DMP tools and templates have developed independently, to a varying degree, allowing for the creation of genuinely machine actionable DMPs. Here we will describe the first three tools or projects for creating maDMPs that were central parts of the original task group mission. We will get into a more detailed account of one of these, specifically the Stockholm University – EOSC Nordic maDMP project using the DMP Online tool, as described by Philipson (2021). We will also briefly touch upon some other current tools and projects for creating maDMPs that are compliant with the RDA DMP Common Standard (RDCS), aiming for integration with other research information systems or research data repositories. A possible conclusion from this overview is that the development of tools for maDMPs is progressing fast and seems to converge towards a common standard. Nonetheless, there remains","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71068287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning","authors":"Junzhou Li, Qianhui Sun, Feixian Sun","doi":"10.5334/dsj-2023-031","DOIUrl":"https://doi.org/10.5334/dsj-2023-031","url":null,"abstract":"","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71068375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RDM+PM Checklist: Towards a Measure of Your Institution’s Preparedness for the Effective Planning of Research Data Management","authors":"Matthew I. Bellgard, Ryan Bennett, Yvette Wyborn, Chris Williams, Leonie Barner, Nikolajs Zeps","doi":"10.5334/dsj-2023-036","DOIUrl":"https://doi.org/10.5334/dsj-2023-036","url":null,"abstract":"A review at our institution and a number of other Australian universities was conducted to identify an optimal institutional-wide approach to Research Data Management (RDM). We found, with a few notable exceptions, a lack of clear policies and processes across institutes and no harmonisation in the approaches taken. We identified limited methods in place to cater for the development of Research Data Management Plans (RDMPs) across different disciplines, project types and no identifiable business intelligence (BI) for auditing or oversight. When interviewed, many researchers were not aware of their institution&rsquo;s RDM policy, whilst others did not understand how it was relevant to their research. It was also discovered that primary materials (PM), which are often directly linked to the effective management of research data, were not well covered. Additionally, it was unclear in understanding who was the data custodian responsible for overall oversight, and there was a lack of clear guidance on the roles and responsibilities of researchers and their supervisors. These findings indicate that institutions are at risk in terms of meeting regulatory requirements and managing data effectively and safely. In this paper, we outline an alternative approach focusing on RDM &lsquo;Planning&rsquo; rather than on RDMPs themselves. We developed simple-to-understand guidance for researchers on the redeveloped RDM policy, which was implemented via an online &lsquo;RDM+PM Checklist&rsquo; tool that guides researchers and students. Moreover, as it is a structured tool, it provides real-time business intelligence that can be used to measure how compliant the organisation is and ideally identify opportunities for continuous improvement.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135838311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}