scholarly journals SSM: A Semantic Metasearch Platform for Scientific Data retrieval

2022 ◽  
Vol 29 (1) ◽  
pp. 91-101
Author(s):  
Gustavo Caetano Borges ◽  
Julio Cesar Dos Reis ◽  
Claudia Bauzer Medeiros
Keyword(s):  
Scientific Research ◽  
Data Retrieval ◽  
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 the agriculture domain.

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.

scholarly journals Mediating Corporeality: Re-Interpretations at the Art/Science Interface

Somatechnics ◽  
2012 ◽  
Vol 2 (2) ◽  
pp. 284-304
Author(s):  
Patricia Adams
Keyword(s):  
Digital Imaging ◽  
Scientific Research ◽  
Digital Technologies ◽  
Adult Stem Cell ◽  
Image Data ◽  
Scientific Data ◽  
Research Data ◽  
The Self ◽  
First Century ◽  
Individual Perspective

Contemporary scientific discoveries are rapidly modifying established concepts of embodiment and corporeality. For example, developing techniques in adult stem cell research can actively remodel the human body; whilst neuroscientists are shedding increasing light on the functioning of our brains. My research at the art/science nexus draws upon recent media theories to investigate the ways twenty-first century constructs of ‘humanness’ and the ‘self’ are affected by both historical and contemporary scientific research and developments in digital imaging technologies. In this article, examples from my artworks: “machina carnis” and “HOST” illustrate how my use of innovative digital technologies and collaborative methodologies has enabled me to immerse myself in the scientific experience at first hand. I demonstrate how my reinterpretations of what is commonly termed ‘hard’ scientific research data does not seek to emulate ‘objective’ readings of the experimental digital image data but rather recontextualises it in the context of my artworks. These artworks acknowledge the personal and visceral content in the scientific data and enable viewer/participants to reflect upon the issues raised from an emotive and individual perspective.

scholarly journals A Content Analysis of Indian Research Data Repositories Prospects and Possibilities

2019 ◽  
Vol 39 (06) ◽  
pp. 280-289 ◽  
Author(s):  
Raj Kumar Bhardwaj
Keyword(s):  
Application Programming Interface ◽  
Research Data ◽  
Identification System ◽  
Microsoft Excel ◽  
Data Repositories ◽  
Data Formats ◽  
Author Identification ◽  
Metadata Standards ◽  
Application Programming ◽  
Content Coverage

The study aims to trace the development of Indian research data repositories (RDRs) and explore their content with the view of identifying prospects and possibilities. Further, it analyses the distribution of data repositories on the basis of content coverage, types of content, author identification system followed, software and the application programming interface used, subject wise number of repositories etc. The study is based on data repositories listed on the registry of data repositories accessible at http://www.re3data.org.The dataset was exported in Microsoft Excel format for analysis. A simple percentage method was followed in data analyses and results are presented through Tables and Figures. The study found a total of 2829 data repositories in existence worldwide. Further, it was seen that 1526 (53.9 %) are open and 924 (32.4 %) are restricted data repositories. Also, there are embargoed data repositories numbering 225 (8.0 %) and closed ones numbering 154 (5.4 %). There are 2829 RDRs covering 72 countries in the world. The study found that out of total 45 Indian RDRs, only 30 (67 %) are open, followed by restricted 12 (27 %) and 3 (6 %) that are closed. Majority of Indian RDRs (20) were developed in the year 2014. The study found that the majority of Indian RDRs (17) are‘disciplinary’. Further, the study also revealed that statistical data formats are available in a maximum of 31 (68.9 %) Indian RDRs. It was also seen that the majority of Indian RDRs (28) has datasets relating to ‘Life Sciences’. It was identified that only 20% of data repositories have been using metadata standards in metadata; the remaining 80% do not use any standards in metadata entry. This study covered only the research data repositories in India registered on the registry of data repositories. RDRs not listed in the registry of data repositories are left out.

Beyond article publishing - support and opportunities for researchers in FAIR data sharing

2020 ◽  
Author(s):  
Graham Smith ◽  
Andrew Hufton
Keyword(s):  
Data Sharing ◽  
San Francisco ◽  
Scientific Data ◽  
Research Data ◽  
Research Assessment ◽  
Environmental Sciences ◽  
Data Repositories ◽  
The Earth ◽  
Data Citation ◽  
The Impact

<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>

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 Curating Scientific Research Data for the Long Term: A Preservation Analysis Method in Context

2011 ◽  
Vol 6 (2) ◽  
pp. 38-52 ◽  
Author(s):  
Esther Conway ◽  
David Giaretta ◽  
Simon Lambert ◽  
Brian Matthews
Keyword(s):  
Scientific Research ◽  
Digital Preservation ◽  
Scientific Data ◽  
Research Data ◽  
Data Sets ◽  
Analysis Method ◽  
Digital Curation ◽  
Analysis Methodology ◽  
Tools And Techniques

The challenge of digital preservation of scientific data lies in the need to preserve not only the dataset itself but also the ability it has to deliver knowledge to a future user community. A true scientific research asset allows future users to reanalyze the data within new contexts. Thus, in order to carry out meaningful preservation we need to ensure that future users are equipped with the necessary information to re-use the data. This paper presents an overview of a preservation analysis methodology which was developed in response to that need on the CASPAR and Digital Curation Centre SCARP projects. We intend to place it in relation to other digital preservation practices, discussing how they can interact to provide archives caring for scientific data sets with the full arsenal of tools and techniques necessary to rise to this challenge.

scholarly journals Data Management and Data Sharing in Psychological Science: Revision of the DGPs Recommendations

2020 ◽  
Author(s):  
Mario Gollwitzer ◽  
Andrea Abele-Brehm ◽  
Christian Fiebach ◽  
Roland Ramthun ◽  
Anne M. Scheel ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Protection ◽  
Scientific Practice ◽  
Data Access ◽  
Scientific Data ◽  
Research Data ◽  
Primary Data ◽  
Practical Implementation ◽  
Data Repositories

Providing access to research data collected as part of scientific publications and publicly funded research projects is now regarded as a central aspect of an open and transparent scientific practice and is increasingly being called for by funding institutions and scientific journals. To this end, researchers should strive to comply with the so-called FAIR principles (of scientific data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on use (embargo period), the definition of "scientific use" by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct "access categories" for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions ("access category 0") to data shared under a set of standardized conditions (e.g., reuse restricted to scientific purposes; "access category 1"), individualized usage agreements ("access category 2"), and secure data access under strictly controlled conditions (e.g., in a research data center; “access category 3"). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.

scholarly journals The Red Queen in the Repository

2020 ◽  
Vol 15 (1) ◽  
pp. 16
Author(s):  
Joakim Philipson
Keyword(s):  
General Purpose ◽  
Research Data ◽  
Data Repository ◽  
Red Queen ◽  
Changing Environment ◽  
Data Repositories ◽  
Metadata Standards ◽  
And Migration ◽  
The Red Queen

One of the grand curation challenges is to secure metadata quality in the ever-changing environment of metadata standards and file formats. As the Red Queen tells Alice in Through the Looking-Glass: “Now, here, you see, it takes all the running you can do, to keep in the same place.” That is, there is some “running” needed to keep metadata records in a research data repository fit for long-term use and put in place. One of the main tools of adaptation and keeping pace with the evolution of new standards, formats – and versions of standards in this ever-changing environment are validation schemas. Validation schemas are mainly seen as methods of checking data quality and fitness for use, but are also important for long-term preservation. We might like to think that our present (meta)data standards and formats are made for eternity, but in reality we know that standards evolve, formats change (some even become obsolete with time), and so do our needs for storage, searching and future dissemination for re-use. Eventually, we come to a point where transformation of our archival records and migration to other formats will be necessary. This could also mean that even if the AIPs, the Archival Information Packages stay the same in storage, the DIPs, the Dissemination Information Packages that we want to extract from the archive are subject to change of format. Further, in order for archival information packages to be self-sustainable, as required in the OAIS model, it is important to take interdependencies between individual files in the information packages into account. This should be done already by the time of ingest and validation of the SIPs, the Submission Information Packages, and along the line at different points of necessary transformation/migration (from SIP to AIP, from AIP to DIP etc.), in order to counter obsolescence. This paper investigates possible validation errors and missing elements in metadata records from three general purpose, multidisciplinary research data repositories – Figshare, Harvard’s Dataverse and Zenodo, and explores the potential effects of these errors on future transformation to AIPs and migration to other formats within a digital archive.  

scholarly journals Over-Professional Skills and Professional Knowledge of Library Specialist: Demands of the Time

2020 ◽  
Vol 68 (6) ◽  
pp. 647-658
Author(s):  
Natalia S. Redkina
Keyword(s):  
Data Management ◽  
Information Technologies ◽  
Professional Knowledge ◽  
Research Data ◽  
Information Resources ◽  
Mobile Technologies ◽  
Process Data ◽  
Data Repositories ◽  
Research Data Management ◽  
Metadata Standards

Library specialists having competencies in the field of modern information technologies and knowledge of information resources, capable to analyse and synthesize heterogeneous information, process data, solve non-standard tasks, are able to develop innovative trends, increase the importance and competitiveness of libraries in the information space. The purpose of this study is to determine the most important skills and knowledge of librarians for the development of new forms and trends in the activities of research libraries: assistant services to scientists, work with research data, creation of intellectual centres, centres of intellectual leisure, organization of communication platforms, etc. The author highlights the key knowledge necessary for librarian: knowledge of modern and advanced information technologies (social networks, cloud, mobile technologies, new generation analytics, etc.), knowledge of the world market of information resources, as well as technologies of collection and processing of information/data. The article presents competences of librarians in the research data management, who provide consulting and assistant services to scientists in the life cycle of research. It is determined that the research data management librarian should know the methods of data management plan preparation, management methods, categories, metadata standards and schemes, data classifications and identifiers, data citation requirements, copyright, data repositories, long-term data preservation technologies, etc. The author concludes that the possession of non-specialized over-professional (“soft”) skills (communication skills, emotional intelligence, thinking by “results” and “processes”, etc.) along with the complex of professional knowledge is the key to the improvement of efficiency and demand of libraries in the conditions of intensively developing environment.

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