Data Management & Open Research Data

Workshops on FAIR Research Data Management and Data Management Plan (DMP)

• DMP or FAIR/Open Research data personalized training for FBM UNIL-CHUV researchers

  Data Management Plan (DMP) accompanying a research project grant application from the SNFS

  Responsable and teacher: Cécile Lebrand, PhD

  Content:

  This workshop in English is a practical training to help researchers from the FBM UNIL and CHUV to fill their Data Management Plan (DMP), a compulsory evolutive document required by the SNSF when applying for a subside for a research project. Link

  During this training, the participants will be help for completing the major DMP fields  of their grant application on mySNF using the UNIL DMP online tool:

  1. Data collection and documentation
  2. Ethical, legal and security issues
  3. Storage and preservation of data
  4. Sharing and re-use of data

  This training is open to all UNIL and CHUV  researchers applying to the SNSF for a basic or clinical research project.

  Practical workshops: Open Data – Publishing research data sets to accompany publications – improving the visibility and reproducibility of your work.

  Responsable and trainer: Cécile Lebrand, PhD

  Content:

  • During this workshop, researchers will be informed about the Open Data guidelines currently being applied by publishing houses and funding agencies.
  • They will learn about the issues involved in sharing the data associated with publications and the resulting benefits for them and for the scientific community in general.
  • They will also learn in practical terms about the importance of metadata, i.e. the information that must accompany their research data so that it can be understood and re-used, as well as the different formats recommended for sharing and long-term access to each type of data they create.
  • Through practical exercises, researchers will be introduced to the use of data repository such as Zenodo, in order to deposit their datasets accompanying their publications, as well as to search for and download data from colleagues that could be useful to them. They will also be introduced to the use of Creative Commons licences to protect their copyright.

  To plan a DMP or FAIR/ORD  training  please contact us by e-mail: Cecile.Lebrand@chuv.ch

• FBM Doctoral School Courses on Research Data Management

  The course “Data Management Plan & Open Data in Life Science” – ED 43/Série 1

  Responsable: Cécile Lebrand, PhD

  Teachers: C. Lebrand, J. Zbinden

  Cours (optionnel)

  Indefinite semester
  Periodic
  Language: English
  Public: Yes
  Credits: 0.25

  Objectif

  At the end of the course participant should be able to put in place a DMP and to share their published data in Open Access, making possible to:
  -respond to the requirements of the journals and financing agencies which require shared standards for open practices in research
  -anticipate in detail the management of research data, specifying how this data is going to be analysed, organised, stored, secured and shared.
  -how to use the tool DMPonline UNIL , an interactive tool designed to help in the (co-)writing, editing and sharing of DMPs.

  Content

  During the first part of this workshop, participants will be taught best practices in data management and how to collect, describe, store, secure and archive research data. they will be introduced to the need for a Data Management Plan (DMP) preparation. The second half of the workshop will be dedicated to practical on Data management, where participants will learn how to fill a DMP corresponding to their thesis research project and how to share their published data on adapted repository.

  Evaluation

  Personal work: No
  Presentation: No
  Final test : No
  Evaluation of participation by the teacher : Yes

• Elixir- Converge course on FAIR Research Data Management and Data Management Plan

  The huge amount of generated research data has urged the scientific community to consider developing efficient FAIR Research Data Management Strategies with an “Open Data” philosophy and implementing robust Data Management Plans (DMP) for research projects. This need is also reflected in the requirements of funding agencies, amongst which the Swiss National Science Foundation (SNFS), Horizon Europe, and publishing platforms. Making research data FAIR – Findable, Accessible, Interoperable and Reusable- provides many benefits, including to increase the visibility and to improve the reproducibility, reuse, and the confidence towards the data, as well as to enable new research questions and collaborations.

  This series of courses, given by researchers and professionals involved in Research Data Management and in Data Management Plan preparation at ELIXIR-CH, SIB/Vital-IT and FBM-UNIL/CHUV, will provide you with the knowledge and the tools to generate robust data and excellent quality studies that follow the FAIR principles. This course will also provide you with effective support to build high quality DMP complying with the guidelines established by funding agencies.

  Module 1: Introduction to FAIR Research Data Management & Data Management Plan

  Courses and materials available in Open Access on Zenodo and Github:

  • https://zenodo.org/record/8159828
  • https://github.com/sib-swiss/Introduction-FAIR-RDM-DMP

  Schedule

  First day- 09:00-17:00 CET

  At first, participants will be introduced to the notion of research reproducibility and to the need for a Data Management Plan (DMP) preparation, an evolving document reporting how the research data will be managed during and after a research project. You will learn best practices in FAIR Research Data Management (RDM) with a focus on data collection and data documentation. During the exercises, participants will directly apply what they have learned.

  In the afternoon, you will learn additional steps of the RDM cycle concerning ethics, legal, security issues, data preservation and data sharing, as well as an overview of the FAIR principles. During the exercises you will learn how to share your published data on adapted repositories, such as Zenodo.

  Second day- 09:00-17:00 CET

  On the second day, participants will learn how to fill a DMP corresponding to their own research using the “Data Stewardship Wizard” tool. Participants will also be able to present and discuss their draft DMPs with the group.

  Audience

  The course is addressed to post-graduate students and researchers who plan to apply for SNFS funds and want to be trained on how to efficiently complete the Data Management Plan form. At the same time, this course aims at educating the participants on FAIR Data Management principles in general.

  Learning objectives

  • At the end of the course you will be able to:
  • Understand the requirements of a Data Management Plan (DMP)
  • Manage the main steps of your research rata using good practices and guidelines (RDM)
  • Understand the FAIR guiding principles and Open Data foundations
  • Use the DSW tool to complete your own DMP

  Prerequisites Knowledge / competencies

  To be involved in Life Sciences research.

  Trainers

  • Cécile Lebrand – Head of Open Science service at FBM UNIL/CHUV; Data Steward at UNIRIS
  • Vassilios Ioannidis – Lead Computational Biologist at SIB/Vital-IT; Data Steward – FAIR Specialist at UNIRIS
  • Grégoire Rossier – Training Manager & Project Manager at SIB/Vital-IT & SIB/Training

  Module 2: Make your Research Data Fair

  Audience

  This workshop is addressed to scientists and clinicians in the biomedical field who are involved, at several possible levels, in Research Data Management and would like to know how to make data compliant with RDM good practices and the FAIR principles.

  Learning outcomes

  • At the end of the course, the participants are expected to know:
  • how to optimize the organization of their data and choose the most suitable file formats.
  • what are ontologies, how to choose them, how and when to create a new one.
  • how to document their data by generating a readme file and using appropriate metadata
  •  how to select FAIR data repositories and deposit data there
  • how to perform risk assessment for sensitive data
  •  how to de-identify / anonymize sensitive data

  Prerequisites

  Knowledge / competencies

  • This course is designed for participants who already have basic notions of [Research Data Management and FAIR principles] (https://sib-swiss.github.io/Introduction-FAIR-RDM-DMP/) and would like to apply them on data.
  • Basic knowledge of UNIX would be a desirable addition. Therefore, we suggest you explore our [UNIX Fundamentals e-learning](https://www.sib.swiss/training/course/2012_UNIXF).

  Program – Schedule – CET time zone

  Day 1 (9:00 – 17:00)

  • Module I: Data Type & Organization

  In this module, participants will train good files management practices such as data entry validation, folders organization, file naming, file format, and versioning. In particular, they will learn how to choose appropriate file formats for sharing, and what important in data entry validation / data cleaning.

  • Module II: Ontologies as controlled vocabularies

  In this module, participants will learn the fundamentals of structured knowledge representation, evaluate existing ontologies for domain suitability, and gain practical skills in extending or crafting new ontologies using standard languages. Through hands-on experience, collaboration, and case studies, this course equips learners to enhance data organization, communication, and information retrieval across diverse domains.

  Day 2 (9:00 – 17:00)

  • Module III: Data Documentation

  During this module, participants will enhance their data documentation skills through metadata and readme files, using tools to facilitate efficient data organization, storage, retrieval, and sharing.  Presented resources include specialized metadata standards (Datacite, OME, DDI, MIAME), domain-specific repositories, as well as a user-friendly automated approach to creating readme files.

  • Module IV: Data Protection

  This module focuses on equipping participants with the skills and knowledge needed to handle sensitive information effectively. It covers anonymizing and de-identifying research data to ensure privacy and compliance with ethical and legal guidelines. Participants will learn to assess risks, remove identifiable information, and use privacy-preserving data sharing tools.

  Organizers

  • Vassilios Ioannidis, PhD- Lead Computational Biologist at SIB/Vital-IT; Data Steward – FAIR Specialist at UNIRIS UNIL
  • Cécile Lebrand  , PhD – Head of Open Science service at FBM UNIL/CHUV; Data Steward at UNIRIS UNIL
  • Grégoire Rossier, PhD – Training Manager & Project Manager at SIB/Vital-IT & SIB/Training

   Lecturers

  Day 1

  • Vassilios Ioannidis, PhD- Lead Computational Biologist at SIB/Vital-IT; Data Steward – FAIR Specialist at UNIRIS UNIL
  • Grégoire Rossier, PhD – Training Manager & Project Manager at SIB/Vital-IT & SIB/Training
  • Tarcisio Mendes de Farias – Knowledge Representation Manager at SIB/Vital-IT

  Day 2

  • Vassilios Ioannidis, PhD- Lead Computational Biologist at SIB/Vital-IT; Data Steward – FAIR Specialist at UNIRIS UNIL
  • Cécile Lebrand, PhD – Head of Open Science service at FBM UNIL/CHUV; Data Steward at UNIRIS UNIL
  • Céline Racine, Research Data Management specialist at Unisanté.
  • Prof. Jean Louis Raisaro, Data Science and Research Lead and Senior Scientist at the ICT Department and Precision Medicine Unit of Lausanne University Hospital (CHUV).
  • Prof. Aleksandar Vjestica, Research Group Leader at Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne.

  Application

  The registration fees for academics are **200 CHF** and **1000 CHF** for profit companies.

  While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

  For more information concerning upcoming training course:

  • visit the SIB training website
  • contact us by e-mail: Cecile.Lebrand@chuv.ch

Tools and services for Long Term Storage (LTS) /archiving of your data at UNIL

Archiving your data at UNIL

Through the process of data life cycle management, our team at BiUM is providing information, advice and help to FBM UNIL researchers for long term storage and preservation of their data for free at the DCSR UNIL.

We can provide you with guidance on  how to prepare a Readme file and reorganize your data in order to preserve your work.

1. Informing and guiding you through the process of reorganizing and describing your research data in the form of explanatory documents called “readme file”.
2. Final validation of your readme file before data migration from the DCRS NAS to the LTS platform.

Additional information:

In French: https://www.bium.ch/processus-dhebergement-donnees-de-recherche-stockage-a-long-terme/

In English: https://www.bium.ch/en/process-of-hosting-search-data-for-long-term-storage/

Tool: readme file

A readme file serves as a documentation resource that offers crucial details about a dataset. Its primary purpose is to ensure accurate interpretation and understanding of the data, both for your future reference and for others who may access or collaborate on the data, including when sharing or publishing it. By providing clear and comprehensive information, a readme file enhances the usability, reproducibility, and transparency of the data, contributing to effective data management and research practices.

Key elements that a thorough readme file should contain:

• File Descriptions and Relationships: Provide a succinct description of each data file included, highlighting what specific data it contains. If relevant, describe how these data files relate to tables, figures, or sections within the associated publication. This context helps users understand the purpose and relevance of each data file.
• Tabular Data Details: For tabular data, elucidate the meaning of column headings and row labels. Define any data codes used, including those for missing data, and specify the measurement units applied. This ensures that users can accurately interpret and work with the data.
• Data Processing Steps: Detail any data processing steps that may affect the interpretation of results, especially if these steps are not already explained in the accompanying publication. This transparency aids users in understanding the transformations applied to the data and their potential impact on analysis.
• Associated Datasets: If certain datasets are stored elsewhere but are associated with the data in question, provide a clear description of these datasets and where they can be accessed. This helps users locate related resources and fosters a more comprehensive understanding of the data context.
• Contact Information: Include clear contact information for inquiries or questions related to the data. This ensures that users have a direct point of contact if they need clarification, assistance, or further information about the dataset.

Reference

Dryad recommendations

Recommendation from Cornell University

Readme file template for LTS at UNIL

Template: LongTermStorage_Data_Description_EN

There’s a plan to deploy a user-friendly and automated methodology for generating readme files fro Big Datasets, developed by Professor Aleksandar Vještica at CIG. This innovative approach will empower researchers to adeptly convey crucial details about their datasets through these readme documents. This advancement promises to further enhance data communication and understanding within the FBM UNIL research community.

Journal and funding agencies Open Data policies

• What are the advantages for researchers and the scientific community to make datasets freely accessible and reusable?
• What are the journal guidelines concerning reporting standards and data sharing?
• What are the funding agencies policies concerning data sharing?

Research funding agencies, publishers and institutions increasingly require shared standards for open practices in research.  The list below includes top funders and journal guidelines.

We can provide you with guidance on  how to prepare a Data Management Plan and how share your data through journal publications and selected repositories to meet founding agencies and journal requirements. Trainings concerning these aspects are also provided by our service on regular basis (check our calendar).

• Irreproducibility of published studies in biomedical research

  Irreproducibility of published studies in preclinical and clinical research.

  Recent studies have shown that worldwide, between 51% to 89% of published preclinical and clinical research is not reproducible, with consequent losses estimated around $100 billions/year in biomedical reerch (Chalmers et al., 2009; Freedman et al., 2015; Begley and Ioannidis, 2015). In particular, these studies have made clear that the research data associated with a publication are fundamental to validate the published analyses and results. Preclinical studies are essential, since they are a potential basis for the discovery of new drugs and therapies, as well as for the use of specific biomarkers in clinical analyses (Begley and Ellis, 2012; Freedman et al., 2015; Begley and Ioannidis, 2015). The low reproducibility rate of research carried out in life sciences is therefore alarming since it causes delays and major costs in therapeutic developments.

  This irreproducibility is not only exclusive to preclinical studies but is observed all across the biomedical research spectrum. Indeed, similar problems have been identified for observational research where zero of 52 predictions from observational studies were confirmed in randomized clinical trials (Begley and Ioannidis, 2015).

  Many causes contribute to this lack of reproducibility in life science studies. For example, researchers often do not conduct their preclinical experiments in a blind manner and therefore tend to identify the results they anticipated (Howells al., 2014; Begley and Ioannidis, 2015). This confirmation bias in scientific research is inevitable and even the best scientists are inclined to unconsciously find results or interpretations that fit their preconceived ideas and theories. A series of recurring problems have also been highlighted, including the lack of sufficient repetition of the number of experiments, the absence of adequate controls, the lack of reagents validation, lack of transparency and standarts while reporting research results and not using appropriate statistical tests (Begley and Ellis, 2012; Howells al., 2014; Begley and Ioannidis, 2015, Holman et al., 2016). In addition to this, researchers often choose the best experience rather than all, and negative results are rarely published. This absence of standards and best practices has led not only to the lack of reproducibility of individual experiences but also to the fact that the main conclusions of the articles are often not correctly documented.

  The irreproducibility of preclinical research is attributed to a lack of both rigor and follow-up of good experimental practices at various stages of the research cycle: i) biological reagents and reference materials, ii) improper preliminary studies design, iii) lack of rigor in data analysis and in reporting research results and iv) random laboratory protocols (Freedman et al., 2015).

  Sources of information

  • Begley, CG, and Ellis L L. “Drug development: Raise standards for preclinical cancer research” Nature. 2012 Mar 28;483(7391):531-3.
  • Begley, C G, and Ioannidis, J. PA. “Reproducibility in science improving the standard for basic and preclinical research.” Circulation research. 2015; 116.1: 116-126.
  • Chalmers I, Glasziou P. Avoidable Waste in the Production and Reporting of Research Evidence. Lancet. 2009; 374(9683): 86–89.
  • Freedman LP, Cockburn IM, Simcoe TS. The Economics of Reproducibility in Preclinical Research. PLoS Biol. 2015;13(6): e1002165.
  • Holman C, Piper S K, Grittner U,  Diamantaras A A , Kimmelman J, Siegerink B, and Dirnagl U “Where Have All the Rodents Gone? The Effects of Attrition in Experimental Research on Cancer and Stroke” PLoS Biol. 2016 Jan 4;14(1):e1002331.
  • Howells, D. W., Sena E.S., and Macleod, M.R. Bringing rigour to translational medicine. Nat Rev Neurol. 2014 Jan;10(1):37-43.
  • Iqbal SA, Wallach JD, Khoury MJ, Schully SD, Ioannidis JPA.”Reproducible Research Practices and Transparency across the Biomedical Literature.” PLoS Biol. 2016. 14(1): e1002333.

• Benefits to data sharing

  Benefits to data sharing

   

  

  Data reuse and citation advantage: Citations are higher for articles that shared data (ex : gene microarray) versus those that do not, independently of the journal IF, the date of publication, and author country of origin (Piwowar H et al. (2007) PLoS ONE).

  Point of view: How open science helps researchers succeed (McKiernan et al. (2016) eLife)

• Open data policies from publishers

  Policies for open research data from publishers

  Too often, publication requirements discourage transparency, openness, and reproducible science. For example, both null and significant results must be made available to ascertain with accuracy the evidence based of a phenomenon. However, as of today, null results are only rarely published and remain inaccessible to knowledge. In addition, to maintain high standards of research reproducibility, and to promote the reuse of new findings, major research data associated with a publication should be made OA according to reporting standards. For this reason, data sharing policies are now often introduced in the instructions for authors by publishers. This requirement is due to the fact that research data are fundamental to validate the analyses and results published in the research article. From this point of view research data are considered as a crucial part of the publication.

  • 02.2015: Science published Scientific Standards Promoting an open research culture

  In November 2014, The Transparency and Openness Promotion (TOP) Committee met at the Center for Open Science in USA, to address the question of journals’ procedures and policies for publication. The committee comprised researchers, journal editors and funding agency representatives. By developing shared standards for open practices across journals, they wished to change the current research incentive system to drive researchers’ attitude toward more openness. They created eight standards in the TOP guidelines which invite scientific communication to move toward greater openness. The TOP guidelines respect barriers limitation to openness by accepting exceptions to sharing due to ethical issues and intellectual property issues or availability of necessary resources. The guidelines have been published in Science in B. A. Nosek et al. Science 2015;348:1422-1425 and are available at at http://cos.io/top, along with a list of 510 top leading journals and 49 organizations that have already agreed to this guideline (September 14 2015).

  Eight standards and three levels of the TOP guidelines provides a summary of the guidelines. “The three levels of the TOP guidelines are increasingly stringent for each standard. Level 0 offers a comparison that does not meet the standard. Two standards (Citation and replication standards) reward researchers for the time and effort they have spent engaging in open practices. Four standards describe what openness means across the scientific process so that research can be reproduced and evaluated. Finally, two standards address the values resulting from preregistration.” Making transparent the distinction between confirmatory and exploratory methods can enhance reproducibility. It is specified that the standard “Design and Analysis Transparency” should maximize transparency about the research process and minimize potential for incomplete reporting of the methodology. This standard is Discipline-specific and the journals should identify if existing reporting guidelines apply (standards available for many research applications from http://www.equator-network.org/) and select the guidelines that are most relevant.

  • 02.2015: NIH adopted the Principles and Guidelines for Reporting Preclinical Research
  • 11.2014: Nature published Journals unite for reproducibility
  • 12.2014 : Science published Journals unite for reproducibility
  • 06.2014: the US National Institute of Neurological Disorders and Stroke organized a meeting with major stakeholders in order to discuss how to improve the methodological reporting of animal studies in funding applications and publications. The main workshop recommendation Principles and Guidelines in Reporting Preclinical Research is that at a minimum “studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. Journals should recommend the deposit of data in open access repositories where available and link data bidirectionally to the published paper. Journals should also strongly encourage, as appropriate, that all materials used in the experiment be shared with those who wish to replicate the experiment.”
  • 06.2010: PLoS Biology: Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research.“The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were introduced to help improve reporting standards in 2010. To maximise their utility, the ARRIVE guidelines have been prepared in consultation with scientists, statisticians, journal editors, and research funders. They were published in PLoS Biology and endorsed by funding agencies and publishers and their journals, including PLoS, Nature research journals, and other top-tier journals”.

  • See data availability policy applies to all of  PLOS journals
  • See policies on availability of data, material and methods from Nature Publishing Group

• DMP and Open data policies from SNSF

  Funding agencies (SNSF and H2020) and institutions (UNIL/CHUV) also strongly encourage authors to provide OA to research data, unless there are strong reasons to restrict access, for example in the case of medical or commercial data. Data privacy for sensitive information related to personal and private information needs to be handled carefully, especially in the biomedical field (see our section on confidentiality and intellectual property). Indeed, the divulgation and open-access of sensitive data implies the explicit consent of the individuals as well as privacy protection through data anonymization. In addition, in case of commercial and patenting issues access to research data may have to be restricted and protected.

  SNSF policy on DMP and Open Research Data

  Research data should be freely accessible to everyone – for scientists as well as for the general public.

  The SNSF agrees with this principle and will introduce new requirements in its project funding scheme as of October 2017. Researchers will have to include a data management plan (DMP) in their funding application. At the same time, the SNSF expects that data generated by funded projects will be publicly accessible in non-commercial and FAIR digital databases provided there are no legal, ethical, copyright or other issues.

  For more detailed information regarding the implementation of the SNSF policy on Open Research Data (ORD), please refer to this webpage.

  We can provide you with guidance on  how to prepare a Data Management Plan and how share your data selected repositories to meet SNSF requirements. Trainings concerning these aspects are  provided by our service on regular basis (check our calendar).

   

  SNSF guidelines for researchers concerning the Data Management Plans (DMPs)

  • Guidelines
  • Content of the DMP
  • Use the “DMP online tool” adapted for  FBM-UNIL/CHUV researchers to make your own DMP template.
  • Contact Cecile.lebrand@chuv.ch to get access to our model on how to fill the Data Management Plan provided by the SNFS while applying for a funding.

  SNSF policy on Open Research Data

  • “Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical).”
    Concordat on Open Research Data, published on 28 July 2016
  • The SNSF values research data sharing as a fundamental contribution to the impact, transparency and reproducibility of scientific research. In addition to being carefully curated and stored, the SNSF believes research data should be shared as openly as possible.
  • The SNSF therefore expects all its funded researchers
  • to store the research data they have worked on and produced during the course of their research work, to share these data with other researchers, unless they are bound by legal, ethical, copyright, confidentiality or other clauses, and
    to deposit their data and metadata onto existing public repositories in formats that anyone can find, access and reuse without restriction.
  • Research data is collected, observed or generated factual material that is commonly accepted in the scientific community as necessary to document and validate research findings.

  SNSF regulations

  The regulations related to the SNSF policy on Open Research Data can be found in the Funding Regulations and in the General Implementation Regulations.

  • FR Art. 47
  • IR Art. 2.13

  SNSF conformed data repositories

  Finding the “perfect” repository providing all necessary features to host FAIR data is challenging. To make the transition towards FAIR research data easier, the SNSF decided to fix a set of minimal criteria that repositories have to fulfil to conform with the FAIR data principles.

  • Guidelines
  • Examples of data repositories (PDF)
  • Dunning et al. (2017), Evaluation of data repositories based on the FAIR Principles for IDCC 2017 practice paper, TU Delft, Data Set, doi:10.4121/uuid:5146dd06-98e4-426c-9ae5-dc8fa65c549f

  Costs for granting access to research data (Open Research Data) – Article 28 paragraph 2 letter c of the Funding Regulations:

  The costs of enabling access to research data that was collected, observed or generated under an SNSF grant are eligible if the following requirements are met: a. The research data is deposited in recognised scientific, digital data archives (data repositories) that meet the FAIR6 principles and do not serve any commercial purpose. b. the costs are specifically related to the preparation of research data in view of its archiving, and to the archiving itself in data repositories pursuant to letter a

• Open data policies from H2020

  Horizon 2020: Open Data Policy

  Since January 2017, all researchers submitting a project proposal in the context of Horizon 2020 have automatically been included in the Open Data pilot.

  • FAIR Data Management in Horizon 2020

• Open data policies and repositories list

  • https://biosharing.org/A curated, informative and educational resource on inter-related data standards, databases, and policies in the life, environmental and biomedical sciences
  • See data availability policy applies to all of  PLOS journals
  • See policies on availability of data, material and methods from Nature Publishing Group
  • SNSF policy on Open Research Data
  • FAIR Data Management in Horizon 2020
  • Data Sharing and Replication from Gary King (go to policies of funding agencies and journal policies)
  • Data Deposit Requirements of Selected Science Journals listed by Victoria
  • Journal open-data policies listed by OAD

Where to publish your datasets: Data repositories

• Where should researchers working at FBM/CHUV deposit their datasets accompanying their publication?
• Which kind of documents can be self-archived?
• Which document format to use for long-term storage?
• What are the copyright and licence legal aspects for datasets?
• When can Open Access to a dataset underlying an article be provided?

• Services
• Zenodo
• Dryad
• figshare

Metadata standards

• Service
• What are Metadata
• Readme file
• Datacite Metadata
• Discipline specific metadata standards

File formats for long-term preservation and re-use

• Service
• FBM Recommended Files formats

Citation for a dataset

Data should be considered legitimate, citable products of research and be given the same importance in the scholarly record as citations of other research objects, such as publications (see Joint Declaration of Data Citation Principles).

Proper citation will help making research data easily accessible and re-usable, while providing researchers due credit for their work. Indeed, since the citation contains the name of the creator, it permits the author to get proper credit. Moreover, the impact of the research dataset can easly be tracked by the unique DOI.

The data citation should be included in the reference list of the article.

• Minimum recommended format

  The minimum recommended format for research data citation is as follows:

  • Creator (PublicationYear): Title. Publisher. Identifier

  Where Publisher is the data archive that holds the data and Identifier is displayed as linkable, permanent URLs.

  For example:

  « Irino, T; Tada, R (2009): Chemical and mineral compositions of sediments from ODP Site 127‐797. Geological Institute, University of Tokyo. http://dx.doi.org/10.1594/PANGAEA.726855 »

  Citation for subject archive entry:

  Genbank accession number, available at: http://www.ncbi.nlm.nih.gov.

  Data citation templates while using citation software:

  • In Endnote use the reference type for dataset.
  • In Mendeley or Zotero, use another generic reference type template and fill it with the information for your dataset.

• Tool DataCite website

  DataCite website

  Access DataCite

  

  Guide « How to Cite Datasets and Link to Publications »

  « This guide is very helpful to create links between your academic publications and the underlying datasets, so that anyone viewing the publication will be able to locate the dataset and vice versa. It provides a working knowledge of the issues and challenges involved, and of how current approaches seek to address them. This guide should interest researchers and principal investigators working on data-led research, as well as the data repositories with which they work“.

Research data confidentiality

FBM/CHUV researchers conducting research on human subjects should consult the Commission cantonale d’éthique de la recherche sur l’être humain before planning research data use and sharing.

In case of concern related to clinical trials issues consult the CRC (Centre de Recherche clinique).

• General protection privacy law

  Swiss researchers must comply with the general protection privacy law regarding Federal Act on Data Protection.

• Federal Act on Research involving Human Beings

  They also need to adhere to the Federal Act on Research involving Human Beings made to protect the dignity, privacy and health of human beings involved in research. Data concerning humans made publicly available must remain totally confidential and be anonymized. Researchers should include a provision for data sharing in the informed consent.

• Sharing Clinical Research Data

  The following article may help you in preparing your data for self-archiving: Hrynaszkiewicz I et al. (2010)

  Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers. Trials 2010 11:9. http://www.trialsjournal.com/content/11/1/9

  See also sharing clinical research data

Intellectual property for datasets

When talking about databases, we first need to distinguish between the structure and the content of a database. The structural elements of a database involving originality will generally be covered by copyright. Concerning the content, individual content items are not copyrightable, while in most juridictions, data collection involving creativity can be copyrightable.

• Services
• Open licenses
• EU directive
• Swiss and UNIL/CHUV directives

Publish your dataset in data journals

Data papers are articles describing a set of data. This new type of papers are designed to make your data more discoverable, interpretable and reusable. Importantly, these papers are found using well developped classical literatture search databases.

• Scientific Data

  Scientific Data is a peer-reviewed open access scientific journal published by the Nature Publishing Group since 2014. Its article-type, the Data Descriptor focuses on descriptions of datasets relevant to the natural sciences. The journal is abstracted and indexed by PubMed.

• GigaScience

  GigaScience is an online open-access open-data journal wishing to revolutionize data dissemination, organization, understanding and use. The journal publishes ‘big-data’ studies from the entire spectrum of life and biomedical sciences. To achieve its goals, the journal has a novel publication format: one that links standard manuscript publication with an extensive database that hosts all associated data and provides data analysis tools and cloud-computing resources.

For more information, see the following list of data journals.

Find datasets on Open Data repositories

• How to find a dataset associated with a published article?

• Services
• biosharing
• re3data
• OpenDOAR
• ROARMAP
• OAD

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