scholarly journals A Universal Standard Archive File for Adsorption Data

2021 ◽  
Author(s):  
Jack D. Evans ◽  
Volodymyr Bon ◽  
Irena Senkovska ◽  
Stefan Kaskel
Keyword(s):  
Open Science ◽  
Text Editor ◽  
Data Format ◽  
Archive File ◽  
Adsorption Data ◽  
Information File ◽  
Database Curation ◽  
Machine Readable ◽  
Data Library ◽  
Universal Standard

<div>New advanced adsorbents are a crucial driver for the development of energy and environmental applications. Tremendous potential is provided by machine learning and data mining techniques, as these approaches can identify the most appropriate adsorbent for a particular application. However, the current scientific reporting of adsorption isotherms in graphs and figures is not adequate to reproduce original experimentally measured data.</div><div><br></div><div>This report proposes the specification of a new standard adsorption information file (AIF) inspired by the ubiquitous crystallographic information file (CIF) and based on the self-defining text archive and retrieval (STAR) procedure, also used to represent biological nuclear magnetic resonance experiments (NMR-STAR). The AIF is a flexible and easily extended free-format archive file that is readily human and machine readable</div><div>and is simple to edit using a basic text editor or parse for database curation. This format represents the first steps toward an open adsorption data format as a basis for a decentralized adsorption data library.</div><div><br></div><div>An open format facilitates the electronic transmission of adsorption data between laboratories, journals and larger databases, which is key in the effort to increase open science in the field of porous materials in the future.</div>

scholarly journals A Universal Standard Archive File for Adsorption Data

2021 ◽  
Author(s):  
Jack D. Evans ◽  
Volodymyr Bon ◽  
Irena Senkovska ◽  
Stefan Kaskel
Keyword(s):  
Open Science ◽  
Text Editor ◽  
Data Format ◽  
Archive File ◽  
Adsorption Data ◽  
Information File ◽  
Database Curation ◽  
Machine Readable ◽  
Data Library ◽  
Universal Standard

<div>New advanced adsorbents are a crucial driver for the development of energy and environmental applications. Tremendous potential is provided by machine learning and data mining techniques, as these approaches can identify the most appropriate adsorbent for a particular application. However, the current scientific reporting of adsorption isotherms in graphs and figures is not adequate to reproduce original experimentally measured data.</div><div><br></div><div>This report proposes the specification of a new standard adsorption information file (AIF) inspired by the ubiquitous crystallographic information file (CIF) and based on the self-defining text archive and retrieval (STAR) procedure, also used to represent biological nuclear magnetic resonance experiments (NMR-STAR). The AIF is a flexible and easily extended free-format archive file that is readily human and machine readable</div><div>and is simple to edit using a basic text editor or parse for database curation. This format represents the first steps toward an open adsorption data format as a basis for a decentralized adsorption data library.</div><div><br></div><div>An open format facilitates the electronic transmission of adsorption data between laboratories, journals and larger databases, which is key in the effort to increase open science in the field of porous materials in the future.</div>

A Universal Standard Archive File for Adsorption Data

Langmuir ◽  
2021 ◽  
Author(s):  
Jack D. Evans ◽  
Volodymyr Bon ◽  
Irena Senkovska ◽  
Stefan Kaskel
Keyword(s):  
Archive File ◽  
Adsorption Data ◽  
Universal Standard

scholarly journals Recommendations for an Open Science approach to welding process research data

2021 ◽  
Author(s):  
Cagtay Fabry ◽  
Andreas Pittner ◽  
Volker Hirthammer ◽  
Michael Rethmeier
Keyword(s):  
Domain Knowledge ◽  
Knowledge Exchange ◽  
Welding Process ◽  
Open Science ◽  
Research Data ◽  
Fusion Welding ◽  
File Format ◽  
Data Format ◽  
Science Practices ◽  
Science Community

AbstractThe increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research.

scholarly journals Data Papers as a New Form of Knowledge Organization in the Field of Research Data

2019 ◽  
Vol 46 (8) ◽  
pp. 622-638
Author(s):  
Joachim Schöpfel ◽  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane
Keyword(s):  
Business Models ◽  
Open Science ◽  
Research Data ◽  
Data Driven ◽  
Academic Publishing ◽  
Science Data ◽  
Research Data Management ◽  
Fair Principles ◽  
New Form ◽  
Machine Readable

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

Shell Processing Support Format for Land 3-D Surveys

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 596-596 ◽  
Author(s):  
Keyword(s):  
Magnetic Media ◽  
Ancillary Data ◽  
Technical Standards ◽  
Data Format ◽  
Wide Acceptance ◽  
Readable Form ◽  
Machine Readable ◽  
Machine Readable Form

The Shell SPS ancillary data format provides a means of encoding field ancillary data on magnetic media in both human readable and machine readable form. The format was defined by groups within Shell Internationale Petroleum Maatschappij in conjunction with comments from other users within the geophysical community. Since the format has gained wide acceptance, the SEG Technical Standards Committee requested that the format be made available publicly by endorsing it as an SEG standard. Shell very graciously agreed to provide this format to the geophysical industry.

scholarly journals OPEN SCIENCE: Where are we? How did we get here? Where to we go from here?

2018 ◽  
Author(s):  
Maryrose Franko
Keyword(s):  
Open Data ◽  
Open Science ◽  
Data Formats ◽  
Informed Decisions ◽  
Scientific Innovation ◽  
Readable Form ◽  
Machine Readable ◽  
The Impact ◽  
Machine Readable Form

The mission of nonprofit funders dictates that research resulting from our funding be freely accessed by our patients and families to help make informed decisions about care, and by our boards and donors to be able to evaluate the impact of our funding. But it is equally critical to our mission that data resulting from our support be published in a machine-readable form and available in easily accessible and open data formats to enable reuse by other researchers. In addition, as technology evolves the need and the ability to share to all research outputs must evolve with it. Only then can the impact of the research be multiplied - increasing the potential for significant and far-reaching advances and scientific innovation.

scholarly journals ELIXIR Software Management Plan for Life Sciences

2021 ◽  
Author(s):  
Renato Alves ◽  
Dimitrios Bampalikis ◽  
Leyla Jael Castro ◽  
José María Fernández ◽  
Jennifer Harrow ◽  
...  
Keyword(s):  
Data Management ◽  
Open Source Software ◽  
Open Science ◽  
Management Plan ◽  
Software Management ◽  
Research Software ◽  
Management Perspective ◽  
Management Plans ◽  
Machine Readable ◽  
Future Plans

Data Management Plans are now considered a key element of Open Science. They describe the data management life cycle for the data to be collected, processed and/or generated within the lifetime of a particular project or activity. A Software Manag ement Plan (SMP) plays the same role but for software. Beyond its management perspective, the main advantage of an SMP is that it both provides clear context to the software that is being developed and raises awareness. Although there are a few SMPs already available, most of them require significant technical knowledge to be effectively used. ELIXIR has developed a low-barrier SMP, specifically tailored for life science researchers, aligned to the FAIR Research Software principles. Starting from the Four Recommendations for Open Source Software, the ELIXIR SMP was iteratively refined by surveying the practices of the community and incorporating the received feedback. Currently available as a survey, future plans of the ELIXIR SMP include a human- and machine-readable version, that can be automatically queried and connected to relevant tools and metrics within the ELIXIR Tools ecosystem and beyond.

scholarly journals BioAssay Templates for the semantic web

2016 ◽  
Vol 2 ◽  
pp. e61 ◽  
Author(s):  
Alex M. Clark ◽  
Nadia K. Litterman ◽  
Janice E. Kranz ◽  
Peter Gund ◽  
Kellan Gregory ◽  
...  
Keyword(s):  
Semantic Web ◽  
Degrees Of Freedom ◽  
Software Tool ◽  
Data Format ◽  
Content Creation ◽  
Software Algorithms ◽  
Pertinent Information ◽  
Low Degrees ◽  
Machine Readable ◽  
Community Input

Annotation of bioassay protocols using semantic web vocabulary is a way to make experiment descriptions machine-readable. Protocols are communicated using concise scientific English, which precludes most kinds of analysis by software algorithms. Given the availability of a sufficiently expressive ontology, some or all of the pertinent information can be captured by asserting a series of facts, expressed as semantic web triples (subject, predicate, object). With appropriate annotation, assays can be searched, clustered, tagged and evaluated in a multitude of ways, analogous to other segments of drug discovery informatics. The BioAssay Ontology (BAO) has been previously designed for this express purpose, and provides a layered hierarchy of meaningful terms which can be linked to. Currently the biggest challenge is the issue of content creation: scientists cannot be expected to use the BAO effectively without having access to software tools that make it straightforward to use the vocabulary in a canonical way. We have sought to remove this barrier by: (1) defining a BioAssay Template (BAT) data model; (2) creating a software tool for experts to create or modify templates to suit their needs; and (3) designing a common assay template (CAT) to leverage the most value from the BAO terms. The CAT was carefully assembled by biologists in order to find a balance between the maximum amount of information captured vs. low degrees of freedom in order to keep the user experience as simple as possible. The data format that we use for describing templates and corresponding annotations is the native format of the semantic web (RDF triples), and we demonstrate some of the ways that generated content can be meaningfully queried using the SPARQL language. We have made all of these materials available as open source (http://github.com/cdd/bioassay-template), in order to encourage community input and use within diverse projects, including but not limited to our own commercial electronic lab notebook products.

Documenting Provenance for Reproducible Marine Ecosystem Assessment in Open Science

2018 ◽  
pp. 1051-1077 ◽  
Author(s):  
Xiaogang Ma ◽  
Stace E. Beaulieu ◽  
Linyun Fu ◽  
Peter Fox ◽  
Massimo Di Stefano ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
Open Science ◽  
Solid Support ◽  
Science Center ◽  
Provenance Information ◽  
Ecosystem Assessment ◽  
Fisheries Science ◽  
Machine Readable ◽  
The U.S ◽  
Community Standard

Open Science not only means the openness of various resources involved in a scientific study but also the connections among those resources that demonstrate the origin, or provenance, of a scientific finding or derived dataset. In this chapter, the authors used the PROV Ontology, a community standard for representing and exchanging machine-readable provenance information in the Semantic Web, and extended it for capturing provenance in the IPython Notebook, a software platform that enables transparent workflows. The developed work was used in conjunction with scientists' workflows in the Ecosystem Assessment Program of the U.S. NOAA Northeast Fisheries Science Center. This work provides a pathway towards formal, well-annotated provenance in an electronic notebook. Not only will the use of such technologies and standards facilitate the verifiability and reproducibility of ecosystem assessments, their use will also provide solid support for Open Science at the interface of science and ecosystem management for sustainable marine ecosystems.

scholarly journals Evolution of Data Formats in Very-High-Energy Gamma-ray Astronomy

Universe ◽  
2021 ◽  
Vol 7 (10) ◽  
pp. 374
Author(s):  
Cosimo Nigro ◽  
Tarek Hassan ◽  
Laura Olivera-Nieto
Keyword(s):  
Open Source ◽  
Gamma Ray ◽  
Future Generation ◽  
Open Science ◽  
High Energy ◽  
Next Generation ◽  
Data Format ◽  
Gamma Ray Astronomy ◽  
Very High Energy ◽  
Scientific Results

Most major scientific results produced by ground-based gamma-ray telescopes in the last 30 years have been obtained by expert members of the collaborations operating these instruments. This is due to the proprietary data and software policies adopted by these collaborations. However, the advent of the next generation of telescopes and their operation as observatories open to the astronomical community, along with a generally increasing demand for open science, confront gamma-ray astronomers with the challenge of sharing their data and analysis tools. As a consequence, in the last few years, the development of open-source science tools has progressed in parallel with the endeavour to define a standardised data format for astronomical gamma-ray data. The latter constitutes the main topic of this review. Common data specifications provide equally important benefits to the current and future generation of gamma-ray instruments: they allow the data from different instruments, including legacy data from decommissioned telescopes, to be easily combined and analysed within the same software framework. In addition, standardised data accessible to the public, and analysable with open-source software, grant fully-reproducible results. In this article, we provide an overview of the evolution of the data format for gamma-ray astronomical data, focusing on its progression from private and diverse specifications to prototypical open and standardised ones. The latter have already been successfully employed in a number of publications paving the way to the analysis of data from the next generation of gamma-ray instruments, and to an open and reproducible way of conducting gamma-ray astronomy.

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