scholarly journals Comparing Open Data Repositories

2021 ◽  
Author(s):  
Pedro H. M. Costa ◽  
André F. R. Cordeiro ◽  
Edson OliveiraJr
Keyword(s):  
Data Management ◽  
Open Data ◽  
Scientific Research ◽  
Open Science ◽  
Data Repositories ◽  
Data Format ◽  
Schema Versioning ◽  
Access Controls ◽  
Storage Limit ◽  
Data Collections

Open Data is one of the main concepts of Open Science, which has the purpose to make scientific research artifacts accessible for everyone. Open data provides recommendations and practices to get access and use data from scientific researches, in a free, permanent, citable, auditable and interchangeable way. To facilitate the data management, it is important to store them in a repository. Considering this context, this paper provides a comparison among five known open data repositories. We performed the comparison taking into account a set of criteria, such as, data format constraints, digital identifier, versioning of published datasets, curators of data collections, metadata schema, versioning and exportation, storage limit, paid services, redundancy and preservation, access controls and APIs. We present results and discussions, in terms of such criteria.

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 CoreTrustSeal

2018 ◽  
Vol 71 (1) ◽  
pp. 162-170 ◽  
Author(s):  
Ingrid Dillo ◽  
Lisa De Leeuw
Keyword(s):  
Open Data ◽  
Open Science ◽  
Self Assessment ◽  
Data Repositories ◽  
Non Profit ◽  
Digital Repositories ◽  
Long Term Storage ◽  
Fair Principles ◽  
Core Characteristics

Open data and data management policies that call for the long-term storage and accessibility of data are becoming more and more commonplace in the research community. With it the need for trustworthy data repositories to store and disseminate data is growing. CoreTrustSeal, a community based and non-profit organisation, offers data repositories a core level certification based on the DSA-WDS Core Trustworthy Data Repositories Requirements catalogue and procedures. This universal catalogue of requirements reflects the core characteristics of trustworthy data repositories. Core certification involves an uncomplicated process whereby data repositories supply evidence that they are sustainable and trustworthy. A repository first conducts an internal self-assessment, which is then reviewed by community peers. Once the self-assessment is found adequate the CoreTrustSeal board certifies the repository with a CoreTrustSeal. The Seal is valid for a period of three years. Being a certified repository has several external and internal benefits. It for instance improves the quality and transparency of internal processes, increases awareness of and compliance with established standards, builds stakeholder confidence, enhances the reputation of the repository, and demonstrates that the repository is following good practices. It is also offering a benchmark for comparison and helps to determine the strengths and weaknesses of a repository. In the future we foresee a larger uptake through different domains, not in the least because within the European Open Science Cloud, the FAIR principles and therefore also the certification of trustworthy digital repositories holding data is becoming increasingly important. Next to that the CoreTrustSeal requirements will most probably become a European Technical standard which can be used in procurement (under review by the European Commission).

scholarly journals Information Integration for Machine Actionable Data Management Plans

2017 ◽  
Vol 12 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Tomasz Miksa ◽  
Andreas Rauber ◽  
Roman Ganguly ◽  
Paolo Budroni
Keyword(s):  
Data Management ◽  
Information Integration ◽  
Open Science ◽  
Management Plan ◽  
Reproducible Research ◽  
Free Form ◽  
Text Documents ◽  
Data Repositories ◽  
Standard Data ◽  
Management Plans

Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obtained from existing tools. We present mapping of tools from the domains of digital preservation, reproducible research, open science, and data repositories to data management plan sections. Thus, we identify the requirements for a good solution and identify its limitations. We also propose a machine-actionable data model that enables information integration. The model uses ontologies and is based on existing standards.

scholarly journals The Open-Specimen Movement

BioScience ◽  
2020 ◽  
Author(s):  
Jocelyn P Colella ◽  
Ryan B Stephens ◽  
Mariel L Campbell ◽  
Brooks A Kohli ◽  
Danielle J Parsons ◽  
...  
Keyword(s):  
Data Management ◽  
Open Data ◽  
Open Science ◽  
Management Plan ◽  
Scientific Data ◽  
Primary Data ◽  
Explicit Integration ◽  
Cultural Shift ◽  
Major Barrier ◽  
Annual Reporting

Abstract The open-science movement seeks to increase transparency, reproducibility, and access to scientific data. As primary data, preserved biological specimens represent records of global biodiversity critical to research, conservation, national security, and public health. However, a recent decrease in specimen preservation in public biorepositories is a major barrier to open biological science. As such, there is an urgent need for a cultural shift in the life sciences that normalizes specimen deposition in museum collections. Museums embody an open-science ethos and provide long-term research infrastructure through curation, data management and security, and community-wide access to samples and data, thereby ensuring scientific reproducibility and extension. We propose that a paradigm shift from specimen ownership to specimen stewardship can be achieved through increased open-data requirements among scientific journals and institutional requirements for specimen deposition by funding and permitting agencies, and through explicit integration of specimens into existing data management plan guidelines and annual reporting.

scholarly journals Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Metabolomics ◽  
2019 ◽  
Vol 15 (10) ◽  
Author(s):  
Kevin M. Mendez ◽  
Leighton Pritchard ◽  
Stacey N. Reinke ◽  
David I. Broadhurst
Keyword(s):  
Cloud Computing ◽  
Web Application ◽  
Data Science ◽  
Open Data ◽  
Open Science ◽  
Data Repository ◽  
Data Repositories ◽  
Fully Integrated ◽  
Computing Platform ◽  
Novices And Experts

Abstract Background A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. Aim of Review To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. Key Scientific Concepts of Review This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

The roles of academic libraries in propagating open science

2016 ◽  
Vol 34 (2) ◽  
pp. 113-121 ◽  
Author(s):  
John I. Ogungbeni ◽  
Amaka R. Obiamalu ◽  
Samuel Ssemambo ◽  
Charles M. Bazibu
Keyword(s):  
Literature Review ◽  
Academic Libraries ◽  
Scientific Collaboration ◽  
Scientific Research ◽  
Open Science ◽  
Scientific Data ◽  
Research Articles ◽  
Data Repositories ◽  
Qualitative Survey ◽  
Key Players

This study investigates the roles of academic libraries in propagating Open Science. The study is a qualitative survey based on literature review. Various definitions of open science from different scholars and schools of thought were examined. Research articles on the effects of open science on research and the place of academic libraries in scientific research were reviewed. Open science enhances collaborations and sharing of resources among researchers. Metadata related activities are more prevalent due to open science. Open science has increased the relevance of science to our environment and world issues like privacy and the rightful author of scientific data are still some of the challenges facing open science. Academic libraries continue to take steps to be involved as key players in the propagation of open science through advocacy, building of institutional data repositories and serving as hubs for scientific collaboration among others. Academic libraries have to do more in the area of advocacy and provision of data repositories.

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.

scholarly journals The establishment of a Norwegian node in RDA

2019 ◽  
Author(s):  
Trond Kvamme ◽  
Philipp Conzett
Keyword(s):  
Data Management ◽  
Data Exchange ◽  
The United States ◽  
Open Science ◽  
Research Data ◽  
Sensitive Data ◽  
Data Repositories ◽  
Management Plans ◽  
Inclusive Approach ◽  
Processing And Storage

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.

scholarly journals Addressing search in scientific open data repositories: A semantic metasearch platform

2021 ◽  
Author(s):  
Gustavo Caetano Borges ◽  
Julio César dos Reis ◽  
Claudia Bauzer Medeiros
Keyword(s):  
Open Data ◽  
Scientific Research ◽  
Scientific Data ◽  
Research Data ◽  
Use Case ◽  
Data Repositories ◽  
Metadata Standards ◽  
Public Repositories

Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in agriculture.

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