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. A 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.
{"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":null,"pages":null},"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}
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. Some 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.
{"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":null,"pages":null},"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}
Fabian Plass, S. Englisch, B. Apeleo Zubiri, Lukas Pflug, E. Spiecker, Michael Stingl
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.
{"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":null,"pages":null},"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}
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
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.
{"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":null,"pages":null},"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}
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.
{"title":"The Launch of the <em>Data Science Journal</em>&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":null,"pages":null},"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}
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
自2019年9月以来，欧洲开放科学云EOSC北欧项目工作包5 (T5.3.2)内的一个任务组将注意力集中在可机器操作的数据管理计划(madmp)上。该小组的一份交付工作文件(Hasan et al. 2021)总结说，从传统的基于自由文本的dmp中提取有用的信息是有问题的。虽然madmp通常更符合FAIR标准，因此对人和机器都可访问，但与其他系统更具互操作性，并为不同的利益相关者提供处理、共享、评估和重用服务。不同的DMP工具和模板在不同程度上独立开发，允许创建真正的机器可操作的DMP。在这里，我们将描述用于创建madmp的前三个工具或项目，它们是原始任务组任务的核心部分。我们将更详细地介绍其中一个，特别是斯德哥尔摩大学- EOSC北欧maDMP项目，使用DMP在线工具，如Philipson(2021)所述。我们还将简要介绍一些其他当前用于创建符合RDA DMP通用标准(RDCS)的madmp的工具和项目，旨在与其他研究信息系统或研究数据存储库集成。从这个概述中可能得出的结论是，madmp工具的开发进展迅速，似乎正在向一个通用标准靠拢。尽管如此，要实现这一目标还有大量的工作要做。*作者关系可在本文后面找到2 Philipson等人。自20世纪60年代末以来，数据管理计划(dmp)一直被用作具有复杂数据管理需求的学科的研究和开发项目管理工具。在这个早期阶段，dmp的发展主要是由对数据管理有特定要求的研究人员推动的(Hasan et al. 2021)。后来，从本世纪初开始，随着开放科学运动的出现，其他利益相关者也加入了dmp。例如，资助组织发布他们自己的DMP模板，表达他们对资助项目的数据管理的要求，以使结果是可验证的透明。出现的其他利益相关者是参与研究管理的学术机构(大学)，以及由政府机构代表的整个社会，他们要求公共资助的研究尽可能公开。这一运动的一部分也是公平原则的问题(Wilkinson et al. 2016)。这反过来又要求dmp成为机器可操作的(madmp)，作为遵守FAIR可访问性和互操作性原则的要求，从而促进与整个研究数据管理基础设施的集成，正如Miksa等人(2019)所建议的那样。为了响应对madmp的呼吁，研究数据联盟(RDA)从2018年开始制定了机器可操作数据管理计划(RDCS)的RDA DMP通用标准，其最新版本(在撰写本文时)是2020年11月11日的1.1版本。遵守RDCS一直是开发用于创建madmp的在线工具的重要指导原则之一。
{"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":null,"pages":null},"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}
Matthew I. Bellgard, Ryan Bennett, Yvette Wyborn, Chris Williams, Leonie Barner, Nikolajs Zeps
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’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 ‘Planning’ rather than on RDMPs themselves. We developed simple-to-understand guidance for researchers on the redeveloped RDM policy, which was implemented via an online ‘RDM+PM Checklist’ 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.
{"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’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 ‘Planning’ rather than on RDMPs themselves. We developed simple-to-understand guidance for researchers on the redeveloped RDM policy, which was implemented via an online ‘RDM+PM Checklist’ 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":null,"pages":null},"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}
Alison Specht, Margaret O’Brien, Rorie Edmunds, Pedro Corrêa, Romain David, Laurence Mabile, Jeaneth Machicao, Yasuhiro Murayama, Shelley Stall
Data Management Plans (DMP) are now a routine part of research proposals but are generally not referred to after funding is granted. The Belmont Forum requires an extensive document, a ‘Data and Digital Object Management Plan’ (D(DO)MP) for its awarded projects that is expected to be kept current over the life of the project. The D(DO)MP is intended to record team decisions about major tools and practices to be used over the life of the project for data and software stewardship, and for preservation of data and software products, aligned with the desired Open Science outcomes relevant to the project. Here we present one of the first instances of the use of Belmont’s D(DO)MP through a case study of the PARSEC project, a multinational and multidisciplinary investigation of the socioeconomic impacts of protected areas. We describe the development and revision of our interpretation of the D(DO)MP and discuss its adoption and acceptance by our research group. We periodically assessed the data management sophistication of team members and their use of the various nominated tools and practices. As a result, for example, we included summaries to enable the key components of the D(DO)MP to be readily viewed by the researcher. To meet the Open Science outcomes in a complex project like PARSEC, a comprehensive and appropriately structured D(DO)MP helps project leaders (a) ensure that team members are committed to the collaboration goals of the project, (b) that there is regular and effective feedback within the team, (c) training in new tools is provided as and when needed, and (d) there is easy access to a short reference to the tools and descriptions of the nominated practices.
{"title":"The Value of a Data and Digital Object Management Plan (D(DO)MP) in Fostering Sharing Practices in a Multidisciplinary Multinational Project","authors":"Alison Specht, Margaret O’Brien, Rorie Edmunds, Pedro Corrêa, Romain David, Laurence Mabile, Jeaneth Machicao, Yasuhiro Murayama, Shelley Stall","doi":"10.5334/dsj-2023-038","DOIUrl":"https://doi.org/10.5334/dsj-2023-038","url":null,"abstract":"Data Management Plans (DMP) are now a routine part of research proposals but are generally not referred to after funding is granted. The Belmont Forum requires an extensive document, a ‘Data and Digital Object Management Plan’ (D(DO)MP) for its awarded projects that is expected to be kept current over the life of the project. The D(DO)MP is intended to record team decisions about major tools and practices to be used over the life of the project for data and software stewardship, and for preservation of data and software products, aligned with the desired Open Science outcomes relevant to the project. Here we present one of the first instances of the use of Belmont’s D(DO)MP through a case study of the PARSEC project, a multinational and multidisciplinary investigation of the socioeconomic impacts of protected areas. We describe the development and revision of our interpretation of the D(DO)MP and discuss its adoption and acceptance by our research group. We periodically assessed the data management sophistication of team members and their use of the various nominated tools and practices. As a result, for example, we included summaries to enable the key components of the D(DO)MP to be readily viewed by the researcher. To meet the Open Science outcomes in a complex project like PARSEC, a comprehensive and appropriately structured D(DO)MP helps project leaders (a) ensure that team members are committed to the collaboration goals of the project, (b) that there is regular and effective feedback within the team, (c) training in new tools is provided as and when needed, and (d) there is easy access to a short reference to the tools and descriptions of the nominated practices.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136006037","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}
The data-driven shift in the science research leads to a wider range of research data. To manage this data in a sustainable and adequate way, data management plans (DMPs) were established as a method. However, some researchers still do not create DMPs due to lack of time, resources and understanding of the needs. Furthermore, most of the existing templates and tools are largely unknown. In this article, we investigated the benefits and challenges of DMPs in two joint research projects of several academic institutions. For this, we described the process during the DMP creation, potential challenges and benefits experienced. We showed that a DMP with completely uniform content among the partner institutions was not possible due to individual and subject differences (e.g., in storage and policies). Instead, individual texts had to be formulated in some cases to overcome the diversity. This complexity could not be handled with the existing tools. Therefore, both projects created an own adapted template with some generic contents. Existing guidelines and internal project policies helped during the generation. We experienced that fewer people work more efficiently on a DMP than many and that all researchers within the project can profit from every individual DMP. Although we were not required to produce one, we recognised the associated benefits as a guide during the research process in joint projects.
{"title":"Benefits and Challenges: Data Management Plans in Two Collaborative Projects","authors":"Denise Jäckel, Anna Lehmann","doi":"10.5334/dsj-2023-025","DOIUrl":"https://doi.org/10.5334/dsj-2023-025","url":null,"abstract":"The data-driven shift in the science research leads to a wider range of research data. To manage this data in a sustainable and adequate way, data management plans (DMPs) were established as a method. However, some researchers still do not create DMPs due to lack of time, resources and understanding of the needs. Furthermore, most of the existing templates and tools are largely unknown. In this article, we investigated the benefits and challenges of DMPs in two joint research projects of several academic institutions. For this, we described the process during the DMP creation, potential challenges and benefits experienced. We showed that a DMP with completely uniform content among the partner institutions was not possible due to individual and subject differences (e.g., in storage and policies). Instead, individual texts had to be formulated in some cases to overcome the diversity. This complexity could not be handled with the existing tools. Therefore, both projects created an own adapted template with some generic contents. Existing guidelines and internal project policies helped during the generation. We experienced that fewer people work more efficiently on a DMP than many and that all researchers within the project can profit from every individual DMP. Although we were not required to produce one, we recognised the associated benefits as a guide during the research process in joint projects.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71068516","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}
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