Dendro: Collaborative Research Data Management Built on Linked Open Data

2014 ◽  
pp. 483-487 ◽  
Author(s):  
João Rocha da Silva ◽  
João Aguiar Castro ◽  
Cristina Ribeiro ◽  
João Correia Lopes
Keyword(s):  
Data Management ◽  
Collaborative Research ◽  
Open Data ◽  
Research Data ◽  
Linked Open Data ◽  
Research Data Management

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.

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 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?

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.

scholarly journals Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data

2021 ◽  
Vol 3 (1) ◽  
pp. 189-204
Author(s):  
Hua Nie ◽  
Pengcheng Luo ◽  
Ping Fu
Keyword(s):  
Data Management ◽  
Open Data ◽  
Implementation Process ◽  
Open Science ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management ◽  
University Library ◽  
Open Research ◽  
Peking University

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.

Managing collaborative research data for integrated, interdisciplinary environmental research

2020 ◽  
Author(s):  
Michael Finkel ◽  
Albrecht Baur ◽  
Tobias K.D. Weber ◽  
Karsten Osenbrück ◽  
Hermann Rügner ◽  
...  
Keyword(s):  
Data Management ◽  
Collaborative Research ◽  
Large Scale ◽  
Full Range ◽  
Research Data ◽  
Environmental Research ◽  
Data Types ◽  
Research Data Management ◽  
Management Concepts

<p>The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management.We present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata.</p>

Research Data Sharing and Reuse Through Open Data

2021 ◽  
pp. 284-306
Author(s):  
Abel Christopher M'kulama ◽  
Akakandelwa Akakandelwa
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Storage ◽  
Mixed Methods Research ◽  
Open Data ◽  
Research Process ◽  
Research Data ◽  
Research Data Management ◽  
Inadequate Knowledge ◽  
Self Administered Questionnaire

Research data management is considered a critical step in the research process among researchers. Researchers are required to submit RDM plans with details about data storage, data sharing, and reuse procedures when submitting research proposals for grants. This chapter presents findings of an investigation into the perceptions and practices of ZARI researchers towards research data management. Mixed methods research using a self-administered questionnaire was adopted for data collection. Fifty-one researchers were sampled and recruited for participation into the study. The study established that the majority of the researchers were not depositing their research data in central repositories; data was kept on individual's devices and was therefore not readily available for sharing. The major challenges being faced by researchers included lack of a policy, lack of a repository, and inadequate knowledge in RDM. The study concludes that research data at ZARI was not being professionally managed. The study recommends for formulation of policies, establishment of repository and staff training.

scholarly journals Research Data: Management and Opening. Polish and European Perspectives

2019 ◽  
Vol 57 (1A (113A)) ◽  
pp. 46-55
Author(s):  
Zuzanna Wiorogórska
Keyword(s):  
European Union ◽  
Data Management ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Individual Member ◽  
The European Union ◽  
Member States ◽  
Research Data Management ◽  
The Individual

Purpose/Thesis: This paper attempts to present the trends in management and opening of research data in Poland and the European Union, based on the analysis of the recently published Polish and European acts and documents as well as of other international initiatives which might influence scholarly publishing and scholarly communication.Approach/Methods: An in-depth review of the latest documents was applied. Results and conclusions: I focused on highlighting the key elements of the reviewed documents and initiatives, highlighting the directions for managing and opening of research data they set and the implications they might have for Polish and European science. I also sketched the possible inconsisten­cies between the European and Polish policies related to research data and scholarly communication.Research limitations: The documents investigated for the purpose of this paper were either Polish or provided by the European Union (EU). I have not analyzed the national documents issued by the individual member states of the EU other than Poland. Hence, it is probable that some solutions on research data management and opening already taken on the level of individual member states have not been included in this paper.Practical implications: This paper may encourage a reflection on the relationship between the regulations issued at the European (EU) or at the national (in this case, Polish), and the practices and requirements of scholarly communication which often contradict those regulations.Originality/Value: This is the first analysis of the latest Polish and European documents and initiatives as related to data management and open data (open science).

scholarly journals Data modeling and processing as a library service for digital humanities research data management

2015 ◽  
Author(s):  
Karin Rydving ◽  
Rune Kyrkjebø
Keyword(s):  
Data Management ◽  
Digital Humanities ◽  
Linked Data ◽  
Open Data ◽  
Data Modeling ◽  
Research Data ◽  
Data Conversion ◽  
Research Data Management ◽  
Special Collections ◽  
The University

Our University Library (UBL) have seen the need and potential for strengthening the infrastructure for digital full text resources at the University of Bergen and we wanted better to establish the library’s role in this area.Five years ago, several research communities expressed concerns that it was becoming increasingly difficult to sustain the competence necessary to run and maintain both physical and digital research archives. More specifically a concrete need for supporting XML-based digital humanities text resources was voiced. We felt the UBL could meet this need by providing a new service.A combination of data modeling, data conversion and an active use of open data solutions has in our view shown itself to be an effective solution. We find that in-house data modeling and processing competence is essential in order to cope with tasks connected to digital text and image resources.Our poster will outline our digital service provision by giving selected references and examples.The Wittgenstein Archives at the University of Bergen (WAB) is one example of a recipient of UBL data services. WAB maintains a richly encoded XML version of the complete Nachlass of philosopher Ludwig Wittgenstein.A library web resource building upon library data modeling and conversion is MARCUS, which shows how catalogue data and image data for the University Library’s own manuscript collections and photographic collections are currently digitized and interconnected using electronic representations of documents and Linked Data/RDF (Resource Description Format) metadata. MARCUS meets UBLs long felt need for a unified special collections digital system. This relates not only to document storage, display and dissemination, but also to the library workflow for the special collections. Both WAB and MARCUS benefits, strategically and day-to-day, from the same competencies within the library.We think that a sensible future-oriented solution entails that each institution, to a greater degree than before, works with modeling and conversion of its own data. Our view is that using Linked Data/RDF encoding will pave the way to connecting data sets in such a way that they enrich one another. Rather than functioning as system providers, we envision large institutions processing and sharing open datasets, as well as encouraging and enabling others to do the same.In line with LIBERs Ten recommendations for libraries to get started with research data management our view is that data modeling and data conversion, within the frame of an active use of open data solutions, are services that belongs within the portfolio of the research library. Presented by Irene Eikefjord, Senior Librarian, University of Bergen Library

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