Data management & Open Research Data

Workhops on DMP and FAIR data/ORD

• DMP or FAIR/Open Research data personalized training

  Trainings concerning these aspects in French or English are provided by our service on demand

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

  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.

  • Responsable(s) and teacher: Cécile Lebrand, PhD

  • Sur demande

  • To register, please contact us by e-mail: Cecile.Lebrand@chuv.ch

   

  Workshops pratiques: Open Data – Publier les sets de données de recherche accompagnant la publication – améliorer la visibilité et reproductibilité de ses travaux.

  Contenu

  Au cours de cette formation, les chercheur-euse-s seront informé-e-s sur les directives en cours Open Data des futures des maisons d’édition et des agences de financement.

  Il-elle-s seront informé-e-s également sur les enjeux accompagnant le partage des données associées aux publications et les bénéfices pour eux-elles, mais aussi pour la communauté scientifiques en général, qui en découlent.

  Il-elle-s découvriront aussi en pratique l’importance des métadonnées, c’est-à-dire les informations devant accompagner leurs données de recherche afin que ces dernières soient compréhensibles et ré-utilisables, ainsi que les différents formats conseillés pour le partage et l’accès à long terme de chacun des types de données qu’il-elle-s créent.

  Les chercheur-euse-s seront initié-e-s grâce à des exercices pratiques à l’utilisation de bases de données telle que Zenodo, afin de déposer leurs sets de données accompagnant leurs publications, mais aussi pour rechercher et télécharger les données de collègues qui pourraient leur être utiles. Il-elle-s seront également initié-e-s à l’utilisation des licences Creative Commons utiles afin de protéger leurs droits d’auteur-e-s. (pour plus de détails)

  Le nombre de participant pour les workshops pratiques est limité à 8 (maximum of 8 participants for practical workshops).

  Prochaine(s) session(s) en français (dias et questions possibles en anglais)

  Sur demande

  Durée totale: 3h00

• FBM Doctoral School Courses

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

  Responsable(s): Cécile Lebrand PhD

  Teachers: C. Lebrand, J. Zbinden

  Cours (optionnel)

  Semestre indéterminé
  Apériodique
  Langue(s) d’enseignement: anglais
  Public: Oui
  Crédits: 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 two interactive tool designed to help in the (co-)writing, editing and sharing of DMPs. In particular, it enables a plan to be written in a collaborative manner and to request a review from our service directly from the application.

  Contenu

  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 research project and how to share their published data on adapted repository.

  Evaluation

  Travail personnel : Non
  Présentation : Non
  Test final : Non
  Evaluation de la participation par l’enseignant : Oui

  Bibliographie

  – 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. – Howells, D. W., Sena E.S., and Macleod, M.R. Bringing rigour to translational medicine. Nat Rev Neurol. 2014 Jan;10(1):37-43. https://www.vital-it.ch/research/software/DMPCanvasGenerator https://mantra.edina.ac.uk/ https://zenodo.org/

  Informations supplémentaires

  https://www.bium.ch/en/publication-open-access/data-management/

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

  This course, given by researchers and professionals involved in Data management at SIB/Vital-IT as well as in Data Management Plan preparation at BiUM,  provide researchers with effective support to produce high quality DMP complying with the guidelines established by funding agencies. Importantly, it will provide you with tools to generate robust data and excellent quality studies that are reproducible and reusable.

  Overview

  Recent studies have shown that worldwide, between 51% and 89% of published life sciences research is not reproducible, with consequent losses estimated at around $100 billions/year in biomedical research (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. Many causes contribute to this lack of reproducibility in life science studies such as a lack of rigor in data management and analysis. This extensive problem related to improper research management has urged scientists to consider developing efficient Data Management Plans (DMP) for their research projects, a need that is also reflected in the requirements of funding agencies, amongst which the Swiss National Science Foundation (SNFS) and Horizon 2020.

  This course, given by researchers and professionals involved in Big Data management at SIB/Vital-IT as well as in Data Management Plan preparation at UNIL/CHUV, will provide you with effective support to produce high quality DMP complying with the guidelines established by funding agencies. Importantly, it will provide you with tools to generate robust data and excellent quality studies that are reproducible and reusable.

  Sources of information

  • 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. DOI: 10.1161/CIRCRESAHA.114.303819
  • Chalmers I, Glasziou P. Avoidable Waste in the Production and Reporting of Research Evidence. Lancet. 2009; 374(9683): 86–89. DOI: 10.1016/S0140-6736(09)60329-9
  • Freedman LP, Cockburn IM, Simcoe TS. The Economics of Reproducibility in Preclinical Research. PLoS Biol. 2015;13(6): e1002165. DOI: 10.1371/journal.pbio.1002165

  Schedule

  Day 1- 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 Research Data Management (RDM) and especially two first steps concerning data collection and data documentation. During the exercises, you will directly apply what you have learned.

  During the afternoon, you will learn additional steps of the RDM cycle concerning Ethics, Legal, Security issues, Data Preservation and Data Sharing. The remaining part of the course will be dedicated to demos and exercises on Data management, where you will learn how to share your published data on adapted repositories, such as Zenodo, and how to fill a DMP corresponding to your own research using the “SIB/Vital-IT DMP Canvas Generator tool”.

  Day 2 – 09:00-12:30 CET

  On this half day, you will continue to complete your DPMs and refine the information under the guidance of the teachers. Those who would like will be able to present their draft DMPs in order to concretize their work and generate productive discussions in the group.

  Audience

  The course is addressed to postgraduate 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 Data Management principles in general.

  Learning objectives

  At the end of the course you will be able to:

  • put in place a DMP (data management plan) which fulfills the requirements of the funding agencies such as the SNFS and H2020, which require a DMP to be put in place
  • manage in detail your research data, specifying how your data will be analysed, organised, stored, secured and shared
  • specify the type of data that is going to be created and shared
  • indicate the process to be followed in respect of the budget, intellectual property, and monitoring
  • use the “SIB/Vital-IT DMP Canvas Generator tool” to make your own DMP template.

  Prerequisites

  Knowledge / competencies

  To be involved in Life Sciences research.

  Technical

  You will need a laptop with a web browser installed.

  Trainers

  • Cécile Lebrand – Open Science advocate and information specialist, Bibliothèque Universitaire de Médecine, CHUV
  • Vassilios Ioannidis – SIB/Vital-IT Competence Center, SIB Swiss Institute of Bioinformatics
  • Grégoire Rossier – SIB/Vital-IT Competence Center & SIB Training, SIB Swiss Institute of Bioinformatics

  Application

  This course is free of charges. It is partially sponsored by ELIXIR-CONVERGE. 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.

  Applications to upcoming training should be done via the SIB courses website.

  You will be informed by email of your registration confirmation.

  Venue and time

  To be define

  Additional information

  Coordination: Diana Marek, SIB Training Group

  You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

  Please note that participation in SIB courses is subject to our general conditions.

  SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

  For more information, please contact training@sib.swiss.

  ELIXIR-CONVERGE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020

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

Through the process of data life cycle management, UNIRIS service at UNIL and the BiUM are 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/

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

Data management services at UNIL/CHUV

Other services have been launched at UNIL and CHUV to help researchers manage active research data during the collection, storage, analysis and preservation and archive of data:

• The Archive Service of UNIL

  UNIRIS (The Archive Service of UNIL)

  In order to provide UNIL researchers with a framework for the effective management of their active data, this service develops a five-year roadmap for the management of research data in collaboration with UNIL faculties and internal experts. The service is already providing resources (good practice guides, DMP templates, etc.) via the web, organize trainings (workshops, etc.) for researchers and conference on data management at UNIL. For more information, feel free to contact the service at the following address: https://uniris.unil.ch/researchdata/#contact

• Vital-IT

  Vital-IT is a Competency Centre in Bioinformatics and Computational Biology that provides infrastructure, support and technological R&D for life science and clinical research in Switzerland and internationally. is a plateform helping Swiss researchers for the management, storage, analyses, and publication of genomic, proteomic and metabolomic big datasets. For more information, feel free to contact the service at the following address: http://www.vital-it.ch/about#contact

• DCSR

  Division Informatique/Calcul Scientifique at UNIL

• BioInformatics Competence center

  BioInformatics Competence Center

  The center has the expertise to offer customized data analysis, that go beyond standard protocols, in order to respond to the request of scientists. Support is available for the following Genomics, Proteomics, FACS and CyTOF, Data processing, Web, Image analysis.

• Service de soutien à la recherche clinique

  Centre de Recherche Clinique de Lausanne (CRC). FBM-UNIL/CHUV researchers conducting clinical study research should consult the CRC as earliest as possible when planning a prospective clinical study either interventional (trial) or observational. The CRC can provide them with services spanning from concept/design to publication, including solutions for electronic data capture, data management and statistical analysis.For more information, feel free to contact the service.

• Unité de valorisation des données et des échantillons biologiques (VDE)

  Unité de valorisation des données et des échantillons biologiques (VDE). FBM/CHUV researchers conducting research on human subjects and using samples from the Biobanque Institutionnelle de Lausanne should consult VDE before planning research data use especially to make sure that data are codified and correctly de-identified. For more information, feel free to contact the service

• PACTT

  PACTT (Powering Academia-industry Collaborations and Technology Transfer). PACTT is the joint technology transfer office of the University of Lausanne (UNIL) and the University Hospital of Lausanne (CHUV). Contact us for commercialisation of research results, protection and management of intellectual property, negotiation and management of collaboration contracts with industry and other institutions, or if you need advice with the creation of a start-up company. For more information, feel free to contact the service at the following address: pactt.info@chuv.ch

• CHUV/UNIL IT services

  The IT services of UNIL, FBM and CHUV ensure the storage, back up and preservation of FBM/CHUV data.

• Data Life Cycle Management

  Data Life Cycle Management (DLCM) the Swiss DLCM project provides Swiss researchers, information professionals and other people interested in Research Data Management with good practices, practical resources as well as news in regard to this topic.

The BiUM publication management unit works in close collaboration with UNIL services UNIRIS, PACTT, FBM/CHUV services uDDSP, CRC, VDE, Swiss universities services Vital-IT_SIB and DLCM and IT services of UNIL and CHUV to help you manage your research data.