Research Data and Code Management - an Introduction
Abstract
This is a workshop in two parts. Part 1: Science is becoming increasingly digital, data-driven, and collaborative. This workshop will provide an overview of methods, platforms, and tools to help you manage your digital data and collaborate more efficiently.Digital research data is on the rise: Most research projects generate and/or collect digital data, thus requiring researchers to learn how to handle data responsibly and proactively. Not only do we need to manage and annotate our data; we should also preserve our data and make them available for re-use by publishing them in accordance with FAIR (findability, accessibility, interoperability, and reusability) open data principles.While data management seems to imply plenty of work and little benefit initially, it does come with considerable personal and practical advantages in the longer term: Well-managed and annotated data are easier to sort, retrieve, and understand, thus boosting research efficiency. High-quality data management can shield you from accidental data loss. Well-managed data may also be an advantage when submitting manuscripts, as data quality just might tip the scales in your favor when acceptance is a close call. What is more, good research data management is a vital ingredient in fostering open science because sharing your data responsibly makes it available to the scientific community. Part 1 focusses on the basics of research data management and FAIR data publication. -- Part 2: Research data management begins even before the data is collected: A detailed project plan helps to manage research data and make workflows transparent throughout the entire research cycle. Funding organizations often require a detailed data management plan before the start of a project. A reference to published project plans can also be essential for the subsequent publication of your research results. For this reason, this workshop will focus on data management plans and pre-registrations for efficient and transparent project management.Research data management does not end after data collection but also includes the crucial phase of data analysis. We will therefore also focus on the management of data processing and analysis.By engaging in good project management and describing your entire research workflow transparently and comprehensibly for the scientific community, you not only ensure a high quality of your research but also contribute to open science. Part 2 focusses on code management and planning your research in order to make your research reproducible.
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Kickstart Research Data Management XXXX/XX/XX Dr. Lydia Riedl & Jonas Tschammer eResearch Center & University Library Contact for questions about RDM: [email protected] for questions about Open Data: dat[email protected]
Open Science & ePublishing: [email protected] Program: 09:00-12:00 Fundamentals of RDM with short coffee break 11:00-11:30 Break 11:30-13:00 Live Demo & Practice: FAIR Data Publishing
Open Science & ePublishing: [email protected] Research data: What? Why? How???
Open Science & ePublishing: [email protected] What?
Open Science & ePublishing: [email protected] What? What kind of data do you work with?
Open Science & ePublishing: [email protected] What? 7 Riedl, Lydia. et al (2022). Multimodal speech-gesture training in patients with schizophrenia spectrum disorder: Effects on quality of life and neural processing In: Schizophrenia Research, S. 112–125. https://doi.org/10.1016/j.schres.2022.06.009. fig. 4, CC-BY (Ausschnitt) Xu, Hui. (2018, March 25). Plant diseases diagnosis system (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1206995, CC-BY Riedl, Lydia (2021, June 15). Data from the project "A multimodal speech-gesture training intervention for patients with schizophrenia and its neural underpinnings" (Version 2.0). OSF. https://doi.org/10.17605/OSF.IO/GAEBR, CC-BY Berlin, Staatsbibliothek. "Ms. theol. lat. fol. 1." Staatsbibliothek zu Berlin – Preußischer Kulturbesitz. http://dx.doi.org/10.48643/b4tm-190, CCO
Open Science & ePublishing: [email protected] What? 8 Riedl, Lydia. et al (2022). Multimodal speech-gesture training in patients with schizophrenia spectrum disorder: Effects on quality of life and neural processing In: Schizophrenia Research, S. 112–125. https://doi.org/10.1016/j.schres.2022.06.009. fig. 4, CC-BY (Ausschnitt) Xu, Hui. (2018, March 25). Plant diseases diagnosis system (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1206995, CC-BY Riedl, Lydia (2021, June 15). Data from the project "A multimodal speech-gesture training intervention for patients with schizophrenia and its neural underpinnings" (Version 2.0). OSF. https://doi.org/10.17605/OSF.IO/GAEBR, CC-BY Berlin, Staatsbibliothek. "Ms. theol. lat. fol. 1." Staatsbibliothek zu Berlin – Preußischer Kulturbesitz. http://dx.doi.org/10.48643/b4tm-190, CCO
Open Science & ePublishing: [email protected] Why?
Open Science & ePublishing: [email protected] https://forschungsdaten-thueringen.de/fdm-scarytales.html Let's play...
Open Science & ePublishing: [email protected] What do you do for data management?
Open Science & ePublishing: [email protected] How??? 22 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] How??? 23 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] How??? 24 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] How??? 25 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] 26 Collect & Process
Open Science & ePublishing: [email protected] 27 What file name do you consider suitable? Example 1 a. 2006-03-24_Attachment b. 24 March 2006 Attachment c. 240306att Examples from: The University of Edinburgh: MANTRA – Research Data Management Training: Organising data. http://mantra.edina.ac.uk/organisingdata/ Example 2 a. labtox_recent_110810_old version.sps b. 2010-08-11_bioassay_toxicity_V1.sps c. FFTX_3776438656.sps Collect & Process: Naming
Open Science & ePublishing: [email protected] • Ideally consistent, informative and concise (<25 characters) • Instead of spaces: underscores, hyphens, or "CamelCase" if necessary. • Avoid special characters, e.g.: " “ / \ : ? ‘ < > [ ] & $ • Date format: YYYY-MM-DD, YYYY_MM_DD or YYYYMMDD Collect & Process: Naming for further deepening: Coffee Lecture on file and folder organization by Christian Krippes (JLU Giessen): https://youtu.be/M76gSb9Urmg
Open Science & ePublishing: [email protected] Example: sub-01_ses-test_task-decision_bold.nii keyword: Subject value: 01 keyword: Task value: Task Type suffix: Neuro Imaging Type File extension: Format (here: specific image format) keyword: Measure ment value: Test Measurement Example: “Brain Imaging Data Structure” (BIDS) Collect & Process: Naming
Open Science & ePublishing: [email protected] 30 Collect & Process: Structure
Open Science & ePublishing: [email protected] Describe: Metadata = information about the data “Metadata is inherently independent data that contains structured information about other data or resources and their characteristics.” Metadata information: https://forschungsdaten.info/themen/beschreiben-und-dokumentieren/met adaten-und-metadatenstandards/ https://dataedo.com/cartoon/data-vs-metadata-8, terms of use: https://dataedo.com/cartoon/share
Open Science & ePublishing: [email protected] 39 Data DictionaryREADME Electronic Lab Notebook Code Book Lab Notebook Extensible Markup Language (XML) Jupyter Notebook Describe: Metadata = information about the data
Open Science & ePublishing: [email protected] Where possible, use a subject-specific metadata standard! Describe: Metadata standards “In order to increase the effectiveness of metadata, standardization of the description is absolutely necessary. Through a metadata standard, metadata from different sources can be linked and edited together.” List of subject-specific metadata standards: https://rdamsc.bath.ac.uk/subject-index
Open Science & ePublishing: [email protected] 41 Nutzen Sie, soweit möglich, einen fachspezifischen Metadatenstandard! Describe: Metadatenstandards “Um die Wirksamkeit von Metadaten zu erhöhen, ist eine Standardisierung der Beschreibung unbedingt erforderlich. Durch einen Metadatenstandard können Metadaten aus unterschiedlichen Quellen miteinander verknüpft und gemeinsam bearbeitet werden.” Liste fachspezifischer Metadatenstandards: https://rdamsc.bath.ac.uk/subject-index https://bids.neuroimaging.io/
Open Science & ePublishing: [email protected] •README: general project description •Metadata: author, title, creation time, etc. •associated data, materials, publications… •table of content (including file naming and structure) •Codebook for individual files: intuitive variable names + labels (short description of the variable) Values (description of the values/scale) Missing (definition of missing values, e.g. “NA”) if necessary Item Text (e.g. text of the question in questionnaires) Example from: Westhoff, Christina (2024) https://data.uni-marburg.de/entities/dataset/25ceb086-c e6d-4058-8759-a56b882f511d, CC-BY Detailed README of The Turing Way: https://github.com/the-turing-way/the-turing-way/blob/main/README.md Evaluate the quality of data documentation | Search for and use research data: https://youtu.be/9vj2-X-ZUxU README template: https://osf.io/4tgky/ Describe: comprehensive documentation
Open Science & ePublishing: [email protected] Describe: useful documentation
Open Science & ePublishing: [email protected] BIDS-Example README This dataset contains the Midnight Scanning Club (MSC) data, a dataset focused on the precise characterization of ten individual subjects via collection of large amounts of per-individual data. Each subject underwent twelve separate two-hour scanning sessions. In the first two sessions we collected four T1 images, four T2 images, four MR angiograms, and eight MR venograms. In the last ten sessions we collected five hours of resting-state fMRI data and over five and a half hours of task fMRI data across three different tasks. Participants also underwent extensive neuropsychological testing. These raw data are all provided here. In addition to the raw data, we also provide […] Example from the midnight scan club dataset: doi:10.18112/openneuro.ds000224.v1.0.4 Describe: complete documentation
Open Science & ePublishing: [email protected] BIDS-Example Dataset Description Metadata: └─ my_dataset/ ├─ dataset_description.json ├─ rawdata/ │ ├─ participants.tsv │ ├─ participants.json │ ├─ sub-01/ │ ├─ sub-02/ │ └─ ... Describe: complete documentation
Open Science & ePublishing: [email protected] BIDS-Example Dataset Description { "Name": "The mother of all experiments", "BIDSVersion": "1.6.0", "DatasetType": "raw", "License": "CC0", "Authors": [ "Paul Broca", "Carl Wernicke" … Describe: complete documentation
Open Science & ePublishing: [email protected] BIDS-Example Participants Table Table of content: └─ my_dataset/ ├─ dataset_description.json ├─ rawdata/ │ ├─ participants.tsv │ ├─ participants.json │ ├─ sub-01/ │ ├─ sub-02/ │ └─ ... Describe: complete documentation
Open Science & ePublishing: [email protected] ●describe clearly and in detail ●use a metadata standard ●use version control! Describe: Data Exit Checklist
Open Science & ePublishing: [email protected] How??? 61 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] Share & reuse 62
Open Science & ePublishing: [email protected] Share & reuse: 3-2-1 rule Save… ● at least 3 copies of your data ● on at least 2 different storage media ● of which at least 1 copy is in another location (can be a cloud) Jørgen Stamp, CC BY 2.5
Open Science & ePublishing: [email protected] …raw data? …active data? Where to go with my... …completed data sets? …data about my publication? •Storage space from HRZ Marburg •JupyterHub at the University of Marburg •Open Science Framework OSF Share & reuse: save & publish
Open Science & ePublishing: [email protected] 65 Assmann, C. (2024, Juni 27). Kick-Start ins Forschungsdatenmanagement. Thüringer FDM-Tage, Online. Zenodo. https://doi.org/10.5281/zenodo.12568776, CC-BY Share & reuse: save & publish
Open Science & ePublishing: [email protected] 66 subject specific repositories: e.g., https://mediarep.org/home generic repositories: e.g., https://zenodo.org/ incl. HeFDI community: https://zenodo.org/communities/hefdi/ institutional repositories: e.g., https://open.uni-marburg.de/ Share & reuse: repositories (= data centers)
Open Science & ePublishing: [email protected] 68 The FAIR principles: https://www.go-fair.org/fair-principles/ BERD Course: Make Your Research Reproducible, 2023, Heidi Seibold, https://berd-nfdi.github.io/BERD-reproducible-research-course/, https://doi.org/10.17605/OSF.IO/RUPT7. Share & reuse: FAIR data
Open Science & ePublishing: [email protected] Pampel, H., Vierkant, P., Scholze, F., Bertelmann, R., Kindling, M., Klump, J., … Dierolf, U. (2013). Making Research Data Repositories Visible: The re3data.org Registry. PLoS ONE, 8(11), e78080. http://doi.org/10.1371/journal.pone.0078080, CC-BY Share & reuse: repositories (= data centers) Find a suitable repository: https://www.re3data.org/ https://repositoryfinder.datacite.org 10.5281/zenodo.3946720
Open Science & ePublishing: [email protected] Repositories make your data • Findable: by making the information about your data (metadata) available in a structured manner (e.g. in machine-readable json files) and assigning persistent IDs (usually DOIs) that secure your data in the long term Share & reuse: repositories (= data centers) Individual data records can be found using search engines: https://www.base-search.net/ https://explore.openaire.eu/ https://commons.datacite.org/ https://datasetsearch.research.google.com Pampel, H., Vierkant, P., Scholze, F., Bertelmann, R., Kindling, M., Klump, J., … Dierolf, U. (2013). Making Research Data Repositories Visible: The re3data.org Registry. PLoS ONE, 8(11), e78080. http://doi.org/10.1371/journal.pone.0078080, CC-BY Find a suitable repository: https://www.re3data.org/ https://repositoryfinder.datacite.org 10.5281/zenodo.3946720
Open Science & ePublishing: [email protected] Check the present data set for the FAIR principles: ● Findable ● Accessible ● Interoperable ● Reusable 10 minutes Augustat, Nick; Endres, Dominik; Müller, Erik M. (2023): Uncertainty of treatment efficacy enhances placebo effects on reinforcement learning. data_UMR. 10.17192/fdr/193 Share & reuse: practice Check FAIRness: https://ardc.edu.au/resource/fair -data-self-assessment-tool/
Open Science & ePublishing: [email protected] Ein Statement für mehr Aktivismus: Agreement für einen Kulturwandel in der Wissenschaft: https://unesdoc.unesco.or g/ark:/48223/pf00003799 49.locale=en https://www.jmwiarda.de/2 023/09/19/ein-bisschen-m ehr-aktivismus/ https://coara.eu/agreeme nt/the-agreement-full-text/ https://www.youtube.com/watch?v=I3Wkvx_ZaFo Share & reuse: To make the world a better place…
Open Science & ePublishing: [email protected] Share & reuse: Exercise Data Publication 83 open_UMR
Open Science & ePublishing: [email protected] Tips
Open Science & ePublishing: [email protected] Tip: use converter for data standards
Open Science & ePublishing: [email protected] Tip: create an ORCiD - a person ID for scientists https://orcid.org/
Open Science & ePublishing: [email protected] 87 The official Open Science Website of Marburg University https://www.uni-marburg.de/open-science Tip: use the Marburg Open Science services
Open Science & ePublishing: [email protected] Tip: use the tools of the European Open Science Cloud https://www.youtube.com/watch?v=67mirPt-FhY The EOSC: EU-Node https://open-science -cloud.ec.europa.eu/ dashboard/home
Open Science & ePublishing: [email protected] Tip: use the tools of the GWDG The Academic Cloud of GWDG: https://academicclou d.de/services/
Open Science & ePublishing: [email protected] Tip: general information on RDM https://forschungsdaten.info/
Open Science & ePublishing: [email protected] 100 The slides are available for reuse! https://doi.org/10.5281/zenodo.8375594 Slides
• Dr. Ortrun Brand (FDM) • Dr. Esther Krähwinkel (FDM) • Bernd Nicklas (HRZ) •Tobias Ortwein • Dr. Lydia Riedl (UB) • Jonas Tschammer (UB) 103 https://uni-marburg.de/eresearch Further Consulting Open Science & ePublishing: [email protected]
Kickstart Research Data Management Open Methods & Reproducibility XXXX/XX/XX Dr. Lydia Riedl & Jonas Tschammer eResearch Center & University Library Contact for questions about RDM: [email protected] for questions about Open Data: dat[email protected]
Open Science & ePublishing: [email protected] Program: 09:00-11:30 Introduction Reproducibility with Exercise 11:30-13:00 Data Management Plans & Preregistrations with Exercise
Open Science & ePublishing: [email protected] Research data: Why planning and reproducibility? Why do I need to manage this? How???
Open Science & ePublishing: [email protected] 110 Why planning and reproducibility? https://doi.org/10.1038/533452a
Open Science & ePublishing: [email protected] 111 https://www.youtube.com/watch?v=N2zK3sAtr-4 Why planning and reproducibility?
Open Science & ePublishing: lyd[email protected] „RESEARCH DATA MANAGEMENT includes all measures ‒ even organisational measures extending beyond research activity in the narrow sense ‒ that need to be taken in order to obtain high quality data, to follow good scientific practice within the data life cycle, to make results reproducible, and possibly fulfil existing documentation requirements (e.g. in the health care sector).“ http://www.rfii.de/en/topics/#ResearchData; Enumeration and emphasis by the author plan collect & process analyzedescribe share & reuse Why do I need to manage this?
Open Science & ePublishing: [email protected] RECAP: What do you do for data management?
Open Science & ePublishing: [email protected] How??? 116 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] 124 "Any fool can write code that a computer can understand. Good programmers write code that humans can understand." Martin Fowler, Kent Beck, John Brant, William Opdyke, and Don Roberts (1999) Refactoring: Improving the Design of Existing Code. Addison-Wesley. p. 15 Great crash course for making code human readable: https://russpoldrack.substack.com/p/clean-coding?utm_campaign=post&utm_medium=web Analyze: Code
Open Science & ePublishing: [email protected] 125 id sample_1 sample_2 ====================== 0 10.2 12.4 1 12.7 10.2 2 9.1 15.2 3 10.7 17.4 4 12.5 21.3 5 11.4 14.7 6 12.5 15.0 7 8.0 16.6 8 11.1 18.2 9 11.1 14.5 1 # Import required software packages 2 from scipy import stats 3 from pandas import read_excel, DataFrame 4 5 # Open data file 6 my_data = read_excel( "test_data.xlsx" ) 7 8 # Look at data 9 print(my_data) 10 11 # Make t-test 12 result = stats.ttest_rel ( 13 my_data["sample_1"], 14 my_data["sample_2"] 15 ) 16 17 # Look at test result 18 print(result) 19 Analyze: Code
Open Science & ePublishing: [email protected] 126 JupyterHub of Marburg University: https://www.uni-marburg.de/de/hrz/dienste/interactive-computing Analyze: Code
Open Science & ePublishing: [email protected] 130 id sample_1 sample_2 ====================== 0 10.2 12.4 1 12.7 10.2 2 9.1 15.2 3 10.7 17.4 4 12.5 21.3 5 11.4 14.7 6 12.5 15.0 7 8.0 16.6 8 11.1 18.2 9 11.1 14.5 1 # Import required software packages 2 from scipy import stats 3 from pandas import read_excel, DataFrame 4 5 # Open data file 6 my_data = read_excel( "test_data.xlsx" ) 7 8 # Look at data 9 print(my_data) 10 11 # Make t-test 12 result = stats.ttest_rel ( 13 my_data["sample_1"], 14 my_data["sample_2"] 15 ) 16 17 # Look at test result 18 print(result) 19 Analyze: Code
Open Science & ePublishing: [email protected] 131 image: Heidi Seibold https://doi.org/10.48440/os.helmholtz.061, CC-BY Kickstart: Versioning Great crash course for code management with git: https://russpoldrack.substack.com/p/essen tial-tools-for-writing-better?r=1n86gc&utm _campaign=post&utm_medium=web&tried Redirect=true
Open Science & ePublishing: [email protected] 132 C.0 C.1 C.2 C.3 C.2.1 C.2.2 COMMI TMERGE C.2.1 C.2.2 öffentliches Beispiel-Git-Repo: https://gitlab.uni-marburg.de/loehdene/learning_git Analyze: Versioning
Open Science & ePublishing: [email protected] 136 Code GitLab of Marburg University: https://www.uni-marburg.de/de/hrz/ dienste/versionsverwaltung
Open Science & ePublishing: [email protected] 143 Adapted from Roger D. Peng DOI: 10.1126/science.1213847 (Copyright © 2011, American Association for the Advancement of Science) Analyze: Reproducibility Text publication only OA-Publication + Data + Code Not reproducible Full replication + data + code … in Open Access (OA)
Open Science & ePublishing: [email protected] 144 Vision: Reproduzierbare Paper! https://doi.org/10.55458/neurolibre.00014 Analyze: Virtualization
Open Science & ePublishing: [email protected] How??? 147 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] Plan: Preregistration 156
Open Science & ePublishing: [email protected] Plan: Let’s start with… https://orcid.org/
Open Science & ePublishing: [email protected] Plan: Let’s start with… https://orcid.org/ Exercise: ● Create an ORCiD. OR ● If you already have an ORCiD, check the entries. If necessary, check the sources of the entries.
Open Science & ePublishing: [email protected] Plan: Let’s start with… https://osf.io
Open Science & ePublishing: [email protected] Plan: Let’s start with… https://osf.io
Open Science & ePublishing: [email protected] Plan: Let’s start with… https://osf.io Exercise: ● Log in with your ORCiD. ● Create an initial test project.
Open Science & ePublishing: [email protected] OSF Guide: https://help.osf.io/hc/en-us/articles/360019738834Create-a-Preregistration COS Video Guide for preregistrations: https://www.youtube.com/watch?v=8QK2-udwoK8 Plan: Preregistrations
Open Science & ePublishing: [email protected] Plan: The Entire Project! https://osf.io
Open Science & ePublishing: [email protected] ●write a data management plan ●write and publish a preregistration ●maintain detailed and transparent documentation of the entire project from the start of the project (and publish it) Plan: Data Exit Checklist
Open Science & ePublishing: [email protected] Summary 166 collect & process analyzedescribe share & reuse plan
Open Science & ePublishing: [email protected] Tip: general information on RDM https://forschungsdaten.info/
Open Science & ePublishing: [email protected] Tip: domain specific infos and services NFDI4Culture: https://nfdi4culture.de/ NFDI4Health: https://www.nfdi4health.de/ Text+: https://text-plus.org/ KonsortSWD: https://www.konsortswd.de/
Open Science & ePublishing: [email protected] Tip: collaborate with other scientists and eScientists UNESCO open science outlook 1: status and trends around the world. DOI: https://doi.org/10.54677/GIIC6829
Open Science & ePublishing: [email protected] Tip: Single Workshops on… The Open Science Program of the Marburg University Research Academy (MARA): https://uni-marburg.de/3sYZJI The Open Science Program of the Marburg University Research Academy (MARA): “Von der Idee zur Veröffentlichung: digitale Innovation und gute wissenschaftliche Praxis mit Open Science” ● Forschungsdatenmanagement – Projektund Datenmanagement (FAIR data) ● Forschungsdatenmanagement – Offene Methoden und Reproduzierbarkeit ● Digitale Methoden und Statistik mit Open Source ●Digitale Methoden für die Geisteswissenschaften (digital humanities), siehe auch Projekt HERMES ● Publizieren im Open Access
Open Science & ePublishing: [email protected] 181 Hessische Forschungsdateninfrastruktur: HeFDI Code School https://www.uni-marburg.de/en/hefdi/hefdi-data-event/code-school Tip: Code School
Open Science & ePublishing: [email protected] 182 Local Marburg network: OSIUM https://openscienceinitiativeuniversitymarburg.github.io/ Other local networks and initiatives: https://osf.io/tbkzh/wiki/home/ Tip: learn from your peers & network Open Science in teaching: https://forrt.org/ https://reproducibilitynetwork.de/
Open Science & ePublishing: [email protected] 183 Tip: Other recommended resources Start learning code Basic code learning app: https://www.sololearn.com/de/ (there are multiple tools out there, also including AI support and/or gamification - so look for resources that suit you best and have fun!) Learn coding for research The Turing Way handbook for reproducible data science: https://the-turing-way.netlify.app/index.html NFDI - BERD Academy Self Learn Course: https://berd-nfdi.github.io/BERD-reproducible-research-course/ The Carpentries - data and coding for researchers: https://carpentries.org/ CodeRefinery - advanced coding for researchers: https://coderefinery.org/
Open Science & ePublishing: [email protected] 184 The eResearch Center at Philipps-Universität https://www.uni-marburg.de/eresearch Tip: Project support and collaboration
Open Science & ePublishing: [email protected] 189 The slides are available for reuse! https://doi.org/10.5281/zenodo.8375594 Slides
• Dr. Ortrun Brand (FDM) • Dr. Esther Krähwinkel (FDM) • Bernd Nicklas (HRZ) •Tobias Ortwein • Dr. Lydia Riedl (UB) • Jonas Tschammer (UB) 191 https://uni-marburg.de/eresearch Further Consulting Open Science & ePublishing: [email protected]