scholarly journals NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

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.

Towards a Specialized Environmental Data Portal: Challenges and Opportunities

2020 ◽  
Author(s):  
Ionut Iosifescu-Enescu ◽  
Gian-Kasper Plattner ◽  
Dominik Haas-Artho ◽  
David Hanimann ◽  
Konrad Steffen
Keyword(s):  
Data Management ◽  
Scientific Information ◽  
Central Element ◽  
Research Data ◽  
Environmental Data ◽  
Research Data Management ◽  
Environmental Domain ◽  
Federal Institute ◽  
Wide Range ◽  
Data Portal

<p>EnviDat – www.envidat.ch – is the institutional Environmental Data portal of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. Launched in 2012 as a small project to explore possible solutions for a generic WSL-wide data portal, it has since evolved into a strategic initiative at the institutional level tackling issues in the broad areas of Open Research Data and Research Data Management. EnviDat demonstrates our commitment to accessible research data in order to advance environmental science.</p><p>EnviDat actively implements the FAIR (Findability, Accessibility, Interoperability and Reusability) principles. Core EnviDat research data management services include the registration, integration and hosting of quality-controlled, publication-ready data from a wide range of terrestrial environmental systems, in order to provide unified access to WSL’s environmental monitoring and research data. The registration of research data in EnviDat results in the formal publication with permanent identifiers (EnviDat own PIDs as well as DOIs) and the assignment of appropriate citation information.</p><p>Innovative EnviDat features that contribute to the global system of modern documentation and exchange of scientific information include: (i) a DataCRediT mechanism designed for specifying data authorship (Collection, Validation, Curation, Software, Publication, Supervision), (ii) the ability to enhance published research data with additional resources, such as model codes and software, (iii) in-depth documentation of data provenance, e.g., through a dataset description as well as related publications and datasets, (iv) unambiguous and persistent identifiers for authors (ORCIDs) and, in the medium-term, (v) a decentralized “peer-review” data publication process for safeguarding the quality of available datasets in EnviDat.</p><p>More recently, the EnviDat development has been moving beyond the set of core features expected from a research data management portal with a built-in publishing repository. This evolution is driven by the diverse set of researchers’ requirements for a specialized environmental data portal that formally cuts across the five WSL research themes forest, landscape, biodiversity, natural hazards, and snow and ice, and that concerns all research units and central IT services.</p><p>Examples of such recent requirements for EnviDat include: (i) immediate access to data collected by automatic measurements stations, (ii) metadata and data visualization on charts and maps, with geoservices for large geodatasets, and (iii) progress towards linked open data (LOD) with curated vocabularies and semantics for the environmental domain.</p><p>There are many challenges associated with the developments mentioned above. However, they also represent opportunities for further improving the exchange of scientific information in the environmental domain. Especially geospatial technologies have the potential to become a central element for any specialized environmental data portal, triggering the convergence between publishing repositories and geoportals. Ultimately, these new requirements demonstrate the raised expectations that institutions and researchers have towards the future capabilities of research data portals and repositories in the environmental domain. With EnviDat, we are ready to take up these challenges over the years to come.</p>

scholarly journals Challenges and Practices of Research Data Management in Selected Iraq Universities

2019 ◽  
Vol 39 (06) ◽  
pp. 308-314
Author(s):  
Mahdi Salah Mohammed ◽  
Rafea Ibrahim
Keyword(s):  
Data Management ◽  
Research Data ◽  
Faculty Members ◽  
Data Repositories ◽  
Postgraduate Students ◽  
Research Data Management ◽  
Research Findings ◽  
Management Policies ◽  
Research Organisations

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.

scholarly journals eLabFTW as an Open Science tool to improve the quality and translation of preclinical research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 292
Author(s):  
Michael Hewera ◽  
Daniel Hänggi ◽  
Björn Gerlach ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Data Management ◽  
Open Source ◽  
Academic Research ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Preclinical Research ◽  
Research Data Management ◽  
New Methods ◽  
Set Up

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

scholarly journals The Data Management Skills Support Initiative: Synthesising Postgraduate Training in Research Data Management

2012 ◽  
Vol 7 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Laura Molloy ◽  
Kellie Snow
Keyword(s):  
Data Management ◽  
Management Training ◽  
Skills Training ◽  
Research Data ◽  
Management Skills ◽  
Research Information ◽  
Research Data Management ◽  
Researcher Development ◽  
Development Framework ◽  
Wide Range

This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.

scholarly journals Economía de los datos: reto y oportunidad

COMeIN ◽  
2015 ◽  
Author(s):  
Estefanía Aguilar Moreno ◽  
Eva Ortoll
Keyword(s):  
Data Management ◽  
Open Data ◽  
Research Data ◽  
Research Data Management ◽  
Datos Abiertos

Las políticas y directrices europeas (y por extensión nacionales) sobre datos presentan retos y oportunidades. De forma amplia los datos abiertos (open data) o, de forma más específica, la gestión de datos de investigación (research data management) son dos ámbitos en los que los profesionales de la información tienen un rol que desempeñar, pero, ¿estamos los documentalistas preparados (y dispuestos) a ocupar nuestro papel en el mundo de los datos?¿“las universidades” adaptan con agilidad las competencias acordes a las nuevas demandas profesionales?

Delivering the Next-Generation Research Repository

2019 ◽  
pp. 144-154
Author(s):  
Adi Alter ◽  
Eddie Neuwirth ◽  
Dani Guzman
Keyword(s):  
Data Management ◽  
Research Output ◽  
Scale Up ◽  
Research Data ◽  
Next Generation ◽  
Data Repositories ◽  
Institutional Repositories ◽  
Research Data Management ◽  
Library Research ◽  
Multiple Data

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.

Supporting open data: the key role of data managers

2021 ◽  
Author(s):  
Alice Fremand
Keyword(s):  
Data Management ◽  
Data Science ◽  
New Technologies ◽  
Open Data ◽  
Research Data ◽  
Complex Data ◽  
Data Set ◽  
Data Publication ◽  
World Data ◽  
The World

<p>Open data is not a new concept. Over sixty years ago in 1959, knowledge sharing was at the heart of the Antarctic Treaty which included in article III 1c the statement: “scientific observations and results from Antarctica shall be exchanged and made freely available”. ​At a similar time, the World Data Centre (WDC) system was created to manage and distribute the data collected from the International Geophysical Year (1957-1958) led by the International Council of Science (ICSU) building the foundations of today’s research data management practices.</p><p>What about now? The WDC system still exists through the World Data System (WDS). Open data has been endorsed by a majority of funders and stakeholders. Technology has dramatically evolved. And the profession of data manager/curator has emerged. Utilising their professional expertise means that their role is far wider than the long-term curation and publication of data sets.</p><p>Data managers are involved in all stages of the data life cycle: from data management planning, data accessioning to data publication and re-use. They implement open data policies; help write data management plans and provide advice on how to manage data during, and beyond the life of, a science project. In liaison with software developers as well as scientists, they are developing new strategies to publish data either via data catalogues, via more sophisticated map-based viewer services or in machine-readable form via APIs. Often, they bring the expertise of the field they are working in to better assist scientists satisfy Findable, Accessible, Interoperable and Re-usable (FAIR) principles. Recent years have seen the development of a large community of experts that are essential to share, discuss and set new standards and procedures. The data are published to be re-used, and data managers are key to promoting high-quality datasets and participation in large data compilations.</p><p>To date, there is no magical formula for FAIR data. The Research Data Alliance is a great platform allowing data managers and researchers to work together, develop and adopt infrastructure that promotes data-sharing and data-driven research. However, the challenge to properly describe each data set remains. Today, scientists are expecting more and more from their data publication or data requests: they want interactive maps, they want more complex data systems, they want to query data, combine data from different sources and publish them rapidly.  By developing new procedures and standards, and looking at new technologies, data managers help set the foundations to data science.</p>

scholarly journals Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing

2020 ◽  
Vol 15 (2) ◽  
pp. 168-170
Author(s):  
Jennifer Kaari
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Privacy ◽  
Management Practices ◽  
Open Data ◽  
Scientific Progress ◽  
Management Plan ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management

A Review of: Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196 Abstract Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing. Design – Email survey. Setting – Universities in Egypt, Jordan, and Saudi Arabia. Subjects – Surveys were sent to 4,086 academic faculty researchers. Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository.  Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities. Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.

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