Managing Open and FAIR Data in Geochemistry: Where are we a decade after the Editors Roundtable?

Author(s):  
Steven L Goldstein ◽  
Kerstin Lehnert ◽  
Albrecht W Hofmann

<p>The ultimate goal of research data management is to achieve the long-term utility and impact of data acquired by research projects. Proper data management ensures that all researchers can validate and replicate findings, and reuse data in the quest for new discoveries. Research data need to be open, consistently and comprehensively documented for meaningful evaluation and reuse following domain-specific guidelines, and available for reuse via public data repositories that make them Findable, persistently Accessible, Interoperable, and Reusable (FAIR).</p><p>In the early 2000’s, the development of geochemical databases such as GEOROC and PetDB underscored that the reporting and documenting practices of geochemical data in the scientific literature were inconsistent and incomplete. The original data could often not be recovered from the publications, and essential information about samples, analytical procedures, data reduction, and data uncertainties was missing, thus limiting meaningful reuse of the data and reproducibility of the scientific findings. In order to avoid that such poor scientific practice might potentially damage the health of the entire discipline, we launched the Editors Roundtable in 2007, an initiative to bring together editors, publishers, and database providers to implement consistent publication practices for geochemical data. Recognizing that mainstream scientific journals were the most effective agents to rectify problems in data reporting and implement best practices, members of the Editors Roundtable created and signed a policy statement that laid out ‘Requirements for the Publication of Geochemical Data’ (Goldstein et al. 2014, http://dx.doi.org/10.1594/IEDA/100426). This presentation will examine the impact of this initial policy statement, assess the current status of best practices for geochemical data management, and explore what actions are still needed. </p><p>While the Editors Roundtable policy statement led to improved data reporting practices in some journals, and provided the basis for data submission policies and guidelines of the EarthChem Library (ECL), data reporting practices overall remained inconsistent and inadequate. Only with the formation of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS, www.copdess.org), which extended the Editors Roundtable to include publishers and data facilities across the entire Earth and Space Sciences, along with the subsequent AGU project ‘Enabling FAIR Data’, has the implementation of new requirements by publishers, funders, and data repositories progressed and led to significant compliance with the FAIR Data Principles. Submission of geochemical data to open and FAIR repositories has increased substantially. Nevertheless, standard guidelines for documenting geochemical data and standard protocols for exchanging geochemical data among distributed data systems still need to be defined, and structures to govern such standards need to be identified by the global geochemistry community. Professional societies such as the Geochemical Society, the European Association of Geochemistry, and the International Association of GeoChemistry can and should take a leading role in this process.</p>

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.


2021 ◽  
Author(s):  
Richard Wessels ◽  
Thijmen Kok ◽  
Hans van Melick ◽  
Martyn Drury

<p>Publishing research data in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner is increasingly valued and nowadays often required by publishers and funders. Because experimental research data provide the backbone for scientific publications, it is important to publish this data as FAIRly as possible to enable reuse and citation of the data, thereby increasing the impact of research.</p><p>The structural geology group at Utrecht University is collaborating with the EarthCube-funded StraboSpot initiative to develop (meta)data schemas, templates and workflows, to support researchers in collecting and publishing petrological and microstructural data. This data will be made available in a FAIR manner through the EPOS (European Plate Observing System) data publication chain <span xml:lang="EN-GB"><span>(https://epos-msl.uu.nl/</span></span><span xml:lang="EN-GB"><span>)</span></span><span xml:lang="EN-GB"><span>.</span></span></p><p>The data workflow under development currently includes: a) collecting structural field (meta)data compliant with the StraboSpot protocols, b) creating thin sections oriented in three dimensions by applying a notch system (Tikoff et al., 2019), c) scanning and digitizing thin sections using a high-resolution scanner, d) automated mineralogy through EDS on a SEM, and e) high-resolution geochemistry using a microprobe. The purpose of this workflow is to be able to track geochemical and structural measurements and observations throughout the analytical process.</p><p>This workflow is applied to samples from the Cap de Creus region in northeast Spain. Located in the axial zone of the Pyrenees, the pre-Cambrian metasediments underwent HT-LP greenschist- to amphibolite-facies metamorphism, are intruded by pegmatitic bodies, and transected by greenschist-facies shear zones. Cap de Creus is a natural laboratory for studying the deformation history of the Pyrenees, and samples from the region are ideal to test and refine the data workflow. In particular, the geochemical data collected under this workflow is used as input for modelling the bulk rock composition using Perple_X.    </p><p>In the near future the workflow will be complimented by adding unique identifiers to the collected samples using IGSN (International Geo Sample Number), and by incorporating a StraboSpot-developed application for microscopy-based image correlation. This workflow will be refined and included in the broader correlative microscopy workflow that will be applied in the upcoming EXCITE project, an H2020-funded European collaboration of electron and x-ray microscopy facilities and researchers aimed at structural and chemical imaging of earth materials. </p>


2018 ◽  
Vol 4 (1) ◽  
pp. 68-75 ◽  
Author(s):  
H. Spallek ◽  
S.M. Weinberg ◽  
M. Manz ◽  
S. Nanayakkara ◽  
X. Zhou ◽  
...  

Introduction: Increasing attention is being given to the roles of data management and data sharing in the advancement of research. This study was undertaken to explore opinions and past experiences of established dental researchers as related to data sharing and data management. Methods: Researchers were recruited from the International Association for Dental Research scientific groups to complete a survey consisting of Likert-type, multiple-choice, and open-ended questions. Results: All 42 respondents indicated that data sharing should be promoted and facilitated, but many indicated reservations or concerns about the proper use of data and the protection of research subjects. Many had used data from data repositories and received requests for data originating from their studies. Opinions varied regarding restrictions such as requirements to share data and the time limits of investigator rights to keep data. Respondents also varied in their methods of data management and storage, with younger respondents and those with higher direct costs of their research tending to use dedicated experts to manage their data. Discussion: The expressed respondent support for research data sharing, with the noted concerns, complements the idea of developing managed data clearinghouses capable of promoting, managing, and overseeing the data-sharing process. Knowledge Transfer Statement: Researchers can use the results of this study to evaluate and improve management and sharing of research data. By encouraging and facilitating the data-sharing process, research can advance more efficiently, and research findings can be implemented into practice more rapidly to improve patient care and the overall oral health of populations.


2019 ◽  
Vol 39 (06) ◽  
pp. 308-314
Author(s):  
Mahdi Salah Mohammed ◽  
Rafea Ibrahim

Research emphasises the fundamental role of research data management (RDM) in enhancing academic and scientific research. This paper intended to examine RDM in Iraqi Universities, identify the current challenges of RDM and propose influential RDM practices. Data collection employed a self-administered questionnaires distributed to 155 postgraduate students and 20 faculty members from five universities in Iraq. Research findings revealed that there is a lack of proper RDM. Postgraduate students and researchers were managing their own research data. Main challenges of maintaining a good RDM involve lack of guidelines on effective RDM practices, insufficient of adequate human resources, technological obsolescence, insecure and inefficient infrastructure, lack of financial resources, absence of research data management policies and lack of support by institutional authorities and researchers negatively influenced on research data management. Postgraduate students and researchers recommend building research data repositories and collaboration with other universities and research organisations.


10.29173/iq12 ◽  
2017 ◽  
Vol 41 (1-4) ◽  
pp. 12
Author(s):  
Bhojaraju Gunjal ◽  
Panorea Gaitanou

This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute.


Author(s):  
Adi Alter ◽  
Eddie Neuwirth ◽  
Dani Guzman

Academic libraries are looking for ways to grow their involvement in and scale-up their support for research activities. The successful transition depends to a large extent on the library's ability to systematically manage data, break down information silos and unify workflows across the library, research office and researchers. Data repositories are at the heart of this challenge, yet often institutional repositories are not built to address the needs of modern research data management due to inability to store all research assets, lack of consistent data models, and insufficient workflows. This chapter will present a new approach to research data management that ensures visibility of research output and data, data coherency, and compliance with open access standards. The authors will discuss a ‘Next-Generation Research Repository' that spans multiple data management activities, including automated data capture, metadata enrichment, dissemination, compliance-related workflows, automated publication to scholarly profiles, as well as open integration with the research ecosystem.


KWALON ◽  
2016 ◽  
Vol 21 (1) ◽  
Author(s):  
René van Horik

Summary Nowadays, research without a role for digital data and data analysis tools is barely possible. As a result, we see an increasing interest in research data management, as this enables the replication of research outcomes and the reuse of research data for new research activities. Data management planning outlines how to handle data, both during research and after the research is completed. Trusted data repositories are places were research data are archived and made available for the long term. This article covers the state of the art concerning data management and data repository demands with a focus on qualitative data sets.


2020 ◽  
Author(s):  
Graham Smith ◽  
Andrew Hufton

<p>Researchers are increasingly expected by funders and journals to make their data available for reuse as a condition of publication. At Springer Nature, we feel that publishers must support researchers in meeting these additional requirements, and must recognise the distinct opportunities data holds as a research output. Here, we outline some of the varied ways that Springer Nature supports research data sharing and report on key outcomes.</p><p>Our staff and journals are closely involved with community-led efforts, like the Enabling FAIR Data initiative and the COPDESS 2014 Statement of Commitment <sup>1-4</sup>. The Enabling FAIR Data initiative, which was endorsed in January 2019 by <em>Nature</em> and <em>Scientific Data</em>, and by <em>Nature Geoscience</em> in January 2020, establishes a clear expectation that Earth and environmental sciences data should be deposited in FAIR<sup>5</sup> Data-aligned community repositories, when available (and in general purpose repositories otherwise). In support of this endorsement, <em>Nature</em> and <em>Nature Geoscience</em> require authors to share and deposit their Earth and environmental science data, and <em>Scientific Data</em> has committed to progressively updating its list of recommended data repositories to help authors comply with this mandate.</p><p>In addition, we offer a range of research data services, with various levels of support available to researchers in terms of data curation, expert guidance on repositories and linking research data and publications.</p><p>We appreciate that researchers face potentially challenging requirements in terms of the ‘what’, ‘where’ and ‘how’ of sharing research data. This can be particularly difficult for researchers to negotiate given that huge diversity of policies across different journals. We have therefore developed a series of standardised data policies, which have now been adopted by more than 1,600 Springer Nature journals. </p><p>We believe that these initiatives make important strides in challenging the current replication crisis and addressing the economic<sup>6</sup> and societal consequences of data unavailability. They also offer an opportunity to drive change in how academic credit is measured, through the recognition of a wider range of research outputs than articles and their citations alone. As signatories of the San Francisco Declaration on Research Assessment<sup>7</sup>, Nature Research is committed to improving the methods of evaluating scholarly research. Research data in this context offers new mechanisms to measure the impact of all research outputs. To this end, Springer Nature supports the publication of peer-reviewed data papers through journals like <em>Scientific Data</em>. Analysis of citation patterns demonstrate that data papers can be well-cited, and offer a viable way for researchers to receive credit for data sharing through traditional citation metrics. Springer Nature is also working hard to improve support for direct data citation. In 2018 a data citation roadmap developed by the Publishers Early Adopters Expert Group was published in <em>Scientific Data</em><sup>8</sup>, outlining practical steps for publishers to work with data citations and associated benefits in transparency and credit for researchers. Using examples from this roadmap, its implementation and supporting services, we outline how a FAIR-led data approach from publishers can help researchers in the Earth and environmental sciences to capitalise on new expectations around data sharing.</p><p>__</p><ol><li>https://doi.org/10.1038/d41586-019-00075-3</li> <li>https://doi.org/10.1038/s41561-019-0506-4</li> <li>https://copdess.org/enabling-fair-data-project/commitment-statement-in-the-earth-space-and-environmental-sciences/</li> <li>https://copdess.org/statement-of-commitment/</li> <li>https://www.force11.org/group/fairgroup/fairprinciples</li> <li>https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1</li> <li>https://sfdora.org/read/</li> <li>https://doi.org/10.1038/sdata.2018.259</li> </ol>


2021 ◽  
Author(s):  
Kai Fay ◽  
Julie Goldman

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes student reported data and course generated analytics from January 2018, through July 8, 2020, for the course Best Practices for Biomedical Research Data Management. By comparing the findings from the enrollment period through March 8, 2020 (pre-pandemic) to the period through July 8, 2020 (during-pandemic), the main goal is to investigate potential shifts due to the COVID-19 pandemic.


2019 ◽  
Author(s):  
Trond Kvamme ◽  
Philipp Conzett

Norway has been selected as a new national node in RDA (Research Data Alliance). Until the end of the project in May 2020, the node will be engaging with research communities, supporting national agendas, and contributing to the EU Open Science Strategy to ensure capillary uptake of RDA principles and outputs. Moreover, they will be working to increase the participation in RDA nationally. The Norwegian RDA node (NO-RDA) will be run by a consortium of seven partners, each of them with specific roles in the activities around the node, and led by NSD - Norwegian Centre for Research Data. NO-RDA will focus on supporting the implementation of RDA outputs and recommendations and on areas of strategic importance for the Nordic region, such as Data Management Plans, FAIR Data Stewardship and management of sensitive data in research within the framework of current international and statutory regulations. In addition to NSD the node consists of NTNU, UiB, UiO, UiT, Unit og Uninett/Sigma2. The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data. RDA has a grass-roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects.


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