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Duke R, McCoy R, Risko C, Bursten JRS. Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies. J Am Chem Soc 2024; 146:11579-11591. [PMID: 38640489 DOI: 10.1021/jacs.3c11399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.
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Affiliation(s)
- Rebekah Duke
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Ryan McCoy
- Department of Philosophy, University of Kentucky, Lexington, Kentucky 40508, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Julia R S Bursten
- Department of Philosophy, University of Kentucky, Lexington, Kentucky 40508, United States
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2
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Brinkhaus HO, Rajan K, Schaub J, Zielesny A, Steinbeck C. Open data and algorithms for open science in AI-driven molecular informatics. Curr Opin Struct Biol 2023; 79:102542. [PMID: 36805192 DOI: 10.1016/j.sbi.2023.102542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 02/19/2023]
Abstract
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
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Affiliation(s)
- Henning Otto Brinkhaus
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Jonas Schaub
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Achim Zielesny
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
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Giess T, Itzigehl S, Range J, Schömig R, Bruckner JR, Pleiss J. FAIR and scalable management of small-angle X-ray scattering data. J Appl Crystallogr 2023; 56:565-575. [PMID: 37032968 PMCID: PMC10077856 DOI: 10.1107/s1600576723001577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/21/2023] [Indexed: 04/07/2023] Open
Abstract
A modular and extensible research data management toolbox based on the programming language Python and the widely used computing platform Jupyter Notebook has been established for the acquisition, visualization, analysis and storage of small-angle X-ray scattering data. A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small-angle X-ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS-tools library is a community-driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python-native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS-tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethylammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.
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Affiliation(s)
- Torsten Giess
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Selina Itzigehl
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Richard Schömig
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Johanna R. Bruckner
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
- Correspondence e-mail:
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The current landscape of author guidelines in chemistry through the lens of research data sharing. PURE APPL CHEM 2023. [DOI: 10.1515/pac-2022-1001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Abstract
As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing
landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research.
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Herres-Pawlis S, Bach F, Bruno IJ, Chalk SJ, Jung N, Liermann JC, McEwen LR, Neumann S, Steinbeck C, Razum M, Koepler O. Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices. Angew Chem Int Ed Engl 2022; 61:e202203038. [PMID: 36347644 DOI: 10.1002/anie.202203038] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead-end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab notebooks (ELN) and laboratory information management systems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research.
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Affiliation(s)
- Sonja Herres-Pawlis
- Institut für Anorganische Chemie, RWTH Aachen University, Landoltweg 1A, 52074, Aachen, Germany
| | - Felix Bach
- E-Research, FIZ Karlsruhe-Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Ian J Bruno
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Stuart J Chalk
- Department of Chemistry, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, USA
| | - Nicole Jung
- Institute of Biological and Chemical Systems (IBCS), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Johannes C Liermann
- Johannes Gutenberg University Mainz, Department of Chemistry, Duesbergweg 10-14, 55128, Mainz, Germany
| | - Leah R McEwen
- Cornell University Library, 293 Clark Hall, Ithaca, NY 14853-2501, USA
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessingstr. 1, 07743, Jena, Germany
| | - Matthias Razum
- E-Research, FIZ Karlsruhe-Leibniz Institute for Information Infrastructure, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Oliver Koepler
- Lab Linked Scientific Knowledge, TIB-Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30173, Hannover, Germany
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Duke R, Bhat V, Risko C. Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community. Chem Sci 2022; 13:13646-13656. [PMID: 36544717 PMCID: PMC9710231 DOI: 10.1039/d2sc05142g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/06/2022] [Indexed: 11/11/2022] Open
Abstract
As buzzwords like "big data," "machine learning," and "high-throughput" expand through chemistry, chemists need to consider more than ever their data storage, data management, and data accessibility, whether in their own laboratories or with the broader community. While it is commonplace for chemists to use spreadsheets for data storage and analysis, a move towards database architectures ensures that the data can be more readily findable, accessible, interoperable, and reusable (FAIR). However, making this move has several challenges for those with limited-to-no knowledge of computer programming and databases. This Perspective presents basics of data management using databases with a focus on chemical data. We overview database fundamentals by exploring benefits of database use, introducing terminology, and establishing database design principles. We then detail the extract, transform, and load process for database construction, which includes an overview of data parsing and database architectures, spanning Standard Query Language (SQL) and No-SQL structures. We close by cataloging overarching challenges in database design. This Perspective is accompanied by an interactive demonstration available at https://github.com/D3TaLES/databases_demo. We do all of this within the context of chemical data with the aim of equipping chemists with the knowledge and skills to store, manage, and share their data while abiding by FAIR principles.
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Affiliation(s)
- Rebekah Duke
- Department of Chemistry & Center for Applied Energy Research, University of KentuckyLexington 40506KentuckyUSA
| | - Vinayak Bhat
- Department of Chemistry & Center for Applied Energy Research, University of KentuckyLexington 40506KentuckyUSA
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of KentuckyLexington 40506KentuckyUSA
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Herres‐Pawlis S, Bach F, Bruno IJ, Chalk SJ, Jung N, Liermann JC, McEwen LR, Neumann S, Steinbeck C, Razum M, Koepler O. Mindestinformationsstandards in der Chemie: Ein Appell zum besseren Umgang mit Forschungsdaten. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202203038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Sonja Herres‐Pawlis
- Institut für Anorganische Chemie RWTH Aachen University Landoltweg 1A 52074 Aachen Deutschland
| | - Felix Bach
- E-Research FIZ Karlsruhe – Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
| | - Ian J. Bruno
- Cambridge Crystallographic Data Centre 12 Union Road Cambridge CB2 1EZ UK
| | - Stuart J. Chalk
- Department of Chemistry University of North Florida 1 UNF Drive Jacksonville FL 32224 USA
| | - Nicole Jung
- Institute of Biological and Chemical Systems (IBCS) Karlsruhe Institute of Technology (KIT) Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
| | - Johannes C. Liermann
- Johannes Gutenberg University Mainz Department of Chemistry Duesbergweg 10–14 55128 Mainz Deutschland
| | - Leah R. McEwen
- Cornell University Library 293 Clark Hall Ithaca NY 14853-2501 USA
| | - Steffen Neumann
- Bioinformatics and Scientific Data Leibniz Institute of Plant Biochemistry Weinberg 3 06120 Halle Deutschland
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry Friedrich-Schiller-University Jena Lessingstr. 1 07743 Jena Deutschland
| | - Matthias Razum
- E-Research FIZ Karlsruhe – Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
| | - Oliver Koepler
- Lab Linked Scientific Knowledge TIB – Leibniz Information Centre for Science and Technology Welfengarten 1B 30173 Hannover Deutschland
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Rauh D, Blankenburg C, Fischer TG, Jung N, Kuhn S, Schatzschneider U, Schulze T, Neumann S. Data format standards in analytical chemistry. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-3101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.
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Affiliation(s)
- David Rauh
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Claudia Blankenburg
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Tillmann G. Fischer
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Nicole Jung
- Karlsruhe Institute of Technology, Institute for Chemical and Biological Systems (IBCS-FMS) , Hermann von Helmholtz Platz 1 , 76344 Eggenstein-Leopolshafen , Germany
| | - Stefan Kuhn
- School of Computer Science and Informatics , De Montfort University , Leicester , UK
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie , Julius-Maximilians-Universität Würzburg , Am Hubland , D-97074 Würzburg , Germany
| | - Tobias Schulze
- Department of Effect-Directed Analysis , Helmholtz Centre for Environmental Research – UFZ , Permoserstr. 15, 04318 Leipzig , Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
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9
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van Gend T, Zuiderwijk A. Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse. JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE 2022. [DOI: 10.1177/09610006221101200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands. In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature. Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts. Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university. We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers.
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Affiliation(s)
- Thijmen van Gend
- Delft University of Technology, Faculty of Technology, Policy and Management, The Netherlands
| | - Anneke Zuiderwijk
- Delft University of Technology, Faculty of Technology, Policy and Management, The Netherlands
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10
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Jablonka KM, Patiny L, Smit B. Making the collective knowledge of chemistry open and machine actionable. Nat Chem 2022; 14:365-376. [PMID: 35379967 DOI: 10.1038/s41557-022-00910-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/10/2022] [Indexed: 11/09/2022]
Abstract
Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.
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Affiliation(s)
- Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland
| | - Luc Patiny
- Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland.
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Schröder M, Biskup T. cwepr - A Python package for analysing cw-EPR data focussing on reproducibility and simple usage. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 335:107140. [PMID: 34999309 DOI: 10.1016/j.jmr.2021.107140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 12/22/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
Reproducibility is at the heart of science. Nevertheless, with the advent of computer-based data processing and analysis, most spectroscopists have a hard time fully reproducing a figure from last year's publication starting from the raw data. Unfortunately, this renders their work eventually unscientific. To change this, we need to develop analysis tools that relieve their users from having to trace each processing and analysis step. Furthermore, these tools need to be modular, extendible, and easy to use in order to get used. To this end, we present here the open-source Python package cwepr based on the ASpecD framework for reproducible analysis of spectroscopic data. This package follows best practices of both, science and software development. Key features include an automatically generated gap-less record of each individual processing and analysis step from the raw data to the final published figure. Additionally, it provides a powerful user interface requiring no programming skills of the user. Due to its code quality, modularity, and extensive documentation, it can be easily extended and is actively developed by spectroscopists working in the field. We expect this approach to have a high impact in the field and to help fighting the looming reproducibility crisis in spectroscopy.
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Affiliation(s)
- Mirjam Schröder
- Leibnitz-Institut für Katalyse e.V., Albert-Einstein-Straße 29a, 18059 Rostock, Germany
| | - Till Biskup
- Institut für Physikalische Chemie, Albert-Ludwigs-Universität Freiburg, Albertstraße 21, 79104 Freiburg, Germany.
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12
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FAIR Data Infrastructure. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2022; 182:195-207. [DOI: 10.1007/10_2021_193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Mietchen D, Penev L, Georgiev T, Ovcharova B, Kostadinova I. Open science in practice: 300 published research ideas and outcomes illustrate how RIO Journal facilitates engagement with the research process. RESEARCH IDEAS AND OUTCOMES 2021. [DOI: 10.3897/rio.7.e68595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Since Research Ideas and Outcomes was launched in late 2015, it has stimulated experimentation around the publication of and engagement with research processes, especially those with a strong open science component. Here, we zoom in on the first 300 RIO articles that have been published and elucidate how they relate to the different stages and variants of the research cycle, how they help address societal challenges and what forms of engagement have evolved around these resources, most of which have a nature and scope that would prevent them from entering the scholarly record via more traditional journals. Building on these observations, we describe some changes we recently introduced in the policies and peer review process at RIO to further facilitate engagement with the research process, including the establishment of an article collections feature that allows us to bring together research ideas and outcomes from within one research cycle or across multiple ones, irrespective of where they have been published.
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14
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Wulf C, Beller M, Boenisch T, Deutschmann O, Hanf S, Kockmann N, Kraehnert R, Oezaslan M, Palkovits S, Schimmler S, Schunk SA, Wagemann K, Linke D. A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts. ChemCatChem 2021. [DOI: 10.1002/cctc.202001974] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Christoph Wulf
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
| | - Matthias Beller
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
| | - Thomas Boenisch
- High Performance Computing Center Stuttgart (HLRS) University of Stuttgart Nobelstr. 19 D-70569 Stuttgart Germany
| | - Olaf Deutschmann
- Karlsruher Institut für Technologie (KIT) Kaiserstraße 12 D-76131 Karlsruhe Germany
| | - Schirin Hanf
- Karlsruher Institut für Technologie (KIT) Engesserstr. 15 D-76131 Karlsruhe Germany
| | - Norbert Kockmann
- Biochemical and Chemical Engineering, Equipment Design TU Dortmund University D-44221 Dortmund Germany
| | - Ralph Kraehnert
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstraße 36 D-10623 Berlin Germany
| | - Mehtap Oezaslan
- Institute of Technical Chemistry TU Braunschweig D-38106 Braunschweig Germany
| | - Stefan Palkovits
- Institute for Technical and Macromolecular Chemistry RWTH Aachen University Worringerweg 2 D-52074 Aachen Germany
| | - Sonja Schimmler
- Fraunhofer Institute for Open Communication Systems (FOKUS) Kaiserin-Augusta-Allee 31 D-10589 Berlin Germany
| | - Stephan A. Schunk
- the high throughput experimentation company Kurpfalzring 104 D-69123 Heidelberg Germany
- BASF SE Carl-Bosch Str. 38 D-67056 Ludwigshafen Germany
| | - Kurt Wagemann
- DECHEMA e.V. Theodor-Heuss-Allee 25 D-60486 Frankfurt Germany
| | - David Linke
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
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15
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Herres‐Pawlis S, Liermann JC, Koepler O. Research Data in Chemistry – Results of the first NFDI4Chem Community Survey. Z Anorg Allg Chem 2020. [DOI: 10.1002/zaac.202000339] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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