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Be prepared to share your research data / Leon Osinski
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to share your research data [email protected], TU/e IEC/Library Available under CC BY-SA license, which permits copying and redistributing the material in any medium or format & adapting the material for any purpose, provided the original author and source are credited & you distribute the adapted material under the same license as the original FuTUre Meeting, 27-05-2014
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Presentation at meeting of FuTUre the young professional network of Eindhoven University of Technology [ TU/e ], Tuesday 27 May 2014
Transcript of Be prepared to share your research data / Leon Osinski
to share your research data
[email protected], TU/e IEC/Library Available under CC BY-SA license, which permits copying and redistributing the material in any medium or format & adapting the material for any purpose, provided the original author and source are credited & you distribute the adapted material under the same license as the original
FuTUre Meeting, 27-05-2014
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Short explanation Be prepared.
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Introduction myself: works at Information Expertise Center / Library of TUe Data librarian ; front office 3TU.Datacentrum
Why you should be prepared? [1]
Because this is expected by: Funders [NWO, ZonMW, EC] Journals [here, here, here, here] Professional organizations: VSNU code of
conduct, KNAW Research evaluators: VSNU SEP protocol
2015-2021
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Just as an example: EC NWO: “Bij databestanden worden NWO en de kennisinstelling waar het onderzoek is uitgevoerd, tezamen beschouwd als de ‘producent van de databank’” “NWO kan ter bevordering van de kennisoverdracht van onderzoeksresultaten nadere voorwaarden stellen aan de auteursrechten en de extractierechten op databanken die in het kader van het onderzoek worden gecreëerd. Databanken met bijbehorende toelichting worden in elk geval op door NWO aan te geven wijze beschikbaar gesteld voor gebruik ten behoeve van verder wetenschappelijk onderzoek. De binnen een onderzoeksproject verzamelde gegevens dienen daarom goed en toegankelijk te worden gedocumenteerd en opgeslagen.” ZonMW: ZonMw en subsidieontvanger zijn tezamen ‘producent van de databank’ Subsidieaanvrager moet laten zien in hoeverre het onderzoek met bestaande data kan worden gedaan, door uitbreiding van bestaande data of met een nieuwe dataverzameling Subsidieontvanger stelt een datamanagementplan op waarin aangegeven wordt hoe, wanneer en de wijze waarop data beschikbaar worden gesteld Data dienen volgens de geldende standaarden te worden gedocumenteerd en opgeslagen Financiële afrekening is afhankelijk van het nakomen van de afspraken over datamanagement EC: “… the beneficiary must aim to deposit at the same time the research data needed to validate the results presented in the deposited scientific publications, ideally into a data repository.” [ Model grant agreement, p. 58, italics mine ] “… the notion of ‘publication’ increasingly includes the data underpinning the publication and results presented (…) This data is needed to validate the results presented (…) Beneficiaries are also invited to grant open access to this data, but there is no obligation to do so.” [ Guidelines on open access, p. 7] Standard Evaluation Protocol 2015-2021: “the assessment committee considers the research unit’s policy on research integrity (…) It is interested in how the unit deals with research data, data management and integrity…” zorgvuldig datamanagement ten dienste van wetenschappelijke integriteit Why these requirements from funders, journals and professional organizations? For the sake of scientific integrity implies research results are verifiable implies sharing data, i.e. making data available to others which in turn means or implies data management ; For the sake of re-use of data by others ; your data can be valuable to others or your data are unique and therefore valuable That you must share your data doesn’t always or necessarily means that you have to share your data in open access. However, it means at least that you should be able to hand over your data when being asked for. If not sloppy science. Computer crashes etc. are not accepted as reason not be able to hand over your data. See also: Schuyt-report KNAW, Responsible research data management and the prevention of scientific misconduct (2013) https://www.knaw.nl/en/news/publications/responsible-research-data-management-and-the-prevention-of-scientific-misconduct?set_language=en
EC
Horizon 2020 #1: model grant agreement • “… the beneficiary must aim to deposit at the same time the
research data needed to validate the results presented in the deposited scientific publications, ideally into a data repository.” [ Model grant agreement, p. 58, italics mine ]
• “… the notion of ‘publication’ increasingly includes the data underpinning the publication and results presented (…) This data is needed to validate the results presented (…) Beneficiaries are also invited to grant open access to this data, but there is no obligation to do so.” [ Guidelines on open access, p. 7]
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You can be sure that these requirements will be taken over – more or less – by other funders like NOW. Must aim = inspanningsverplichting ; not mandatory Horizon 2020: budget about 70 miljard euro. Achtse kaderprogramma voor onderzoek, 2014-2020 http://ec.europa.eu/research/participants/portal/desktop/en/funding/reference_docs.html#h2020-mga-gga [ Participant portal Horizon 2020 ] http://ec.europa.eu/research/participants/data/ref/h2020/mga/gga/h2020-mga-gga-mono_en.pdf [ Model Grant Agreement Horizon 2020 ] http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf [ = Guidelines on open access…] Het gaat om onderzoeksdata die aan de resultaten van het onderzoek ten grondslag liggen En omwille van de wetenschappelijke integriteit (validatie, verificatie onderzoeksresultaten)
EC
Horizon 2020 #2: Open research data pilot • “… aims to improve and maximise access to and re-use of research data
generated by projects for the benefit of society and the economy.” • “Regarding the digital research data (…), the beneficiaries must: deposit in a
research data repository and take measures to make it possible (…) to access, mine, exploit, reproduce, and disseminate – free of charge for any user (…) the data … [italics mine]
• Two types of data: data underlying publications and other data as indicated by researchers in DMPs [raw data or curated data not directly attributable to a publication]
• “Participating projects will be required to develop a Data Management Plan (DMP), in which they will specify what data will be open.”
• “Projects may opt out of the pilot to allow for the protection of intellectual property or personal data; in view of security concerns; (…)”
• “As an exception, the beneficiaries do not have to ensure open access to specific parts of their research data…”
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http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf [Guidelines on data management in Horizon 2020 ] Open research data pilot: ook hergebruik van data ; vooral ingevuld door een DMP [ DMP as an early deliverable within the first six months of the project ] Scope: 7 areas of Horizon 2020 ; €3 billion [ 20% of the overall Horizon 2020 budget 2014-2015 ] Guidelines on open access to scientific publications and research data in Horizon 2020 (version 1.0, 11 December 2013) Guidelines on data management in Horizon 2020 (version 1.0, 11 december 2013): open research data pilot Open research data pilot / Data management plan [ DMP ] What types of data will the project generate/collect? What standards will be used? How will this data be exploited and/or shared/made accessible for verification and re-use? If data cannot be made available explain why How will this data be curated and preserved?
Because you benefit from it data sharing increases your visibility ; papers
with data attached are cited more often data sharing is an indication of the
trustworthiness of your research
Why you should be prepared? [2]
Source: Research Data Netherlands / Marina Noordegraaf
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Trustworthiness: something about fraud cases and the severe effects these have for the trust in science
Data sharing implies research data management [RDM] during your research, or: RDM prepares the way for sharing your data after the project RDM: caring* for your data with the purpose of protecting their mere existence and making them available to others
How you can prepare yourself? [1]
Source: Research Data Netherlands / Marina Noordegraaf
*Goodman A, et al. (2014) Ten simple rules for the care and feeding of scientific data. PLoS Comput Biol 10(4): e1003542. doi:10.1371/journal.pcbi.1003542
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This is the main point I like to make: RDM is prepatory to sharing your data! In essence: RDM is about scientific collaboration. Sharing data and workflows enables collaboration. In that way it also improves your research efficiency.
Protection against physical loss and destruction
storage, backup
Protection against intellectual loss and unretrievability
metadata, documentation
Protection against unauthorized use access control
How you can prepare yourself? [2]
Source: Research Data Netherlands / Marina Noordegraaf
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RDM consists of these parts. minimize the risk of data loss or deletion ; protect your data from unauthorized use ; use the correct data. Especially when you edit your data often or collect data through various experiments or tests, identifying the correct data may pose a problem ; RDM enhances the efficiency of your research.
Dataverse Network: data lab for research data where you may store your data in an organized and safe way clearly describe your data arrange access to your data get recognition for your data
How you can prepare yourself? [3]
Source: Research Data Netherlands / Marina Noordegraaf
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A data lab is an electronic environment or platform where you may save your research data in a properly organized and safe way, and where you may clearly describe the data (by assigning metadata) and arrange access to them (access control). In many cases data labs also provide options for working on research data in collaboration with others.� A data lab allows researchers to store, describe, share, keep control and get recognition for their data.
During your research
Dataverse Network
After your research: publishing and archiving in a repository
3TU.Datacentrum, Figshare, Zenodo, DANS, Dryad
After your research: a (data) journal J Open Psychology Data, Geoscience Data J
How you can share your data? [1]
Source: Research Data Netherlands / Marina Noordegraaf
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Also during your research you have to share data. Your promotor wants to take a quick look at your data, or you may want to have an opinion on your data by a colleague. DOI: unique identifier: remains the same, even when the underlying internet address (URL) changes. By publishing your data with a DOI, it allows you or others to: claim data retrieve data cite data let data be reused attach data to his/her publications
“What research data and waste have in common is that’s worthwhile to reuse them.” Lilliana Abarca-Guerrero (2014), A construction waste generation model for developing countries, PhD thesis TU/e, proposition 9
How you can share your data? [2]
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Small but also tedious data
Data Coach Symposium Research data management: funder requirements, questions and solutions Wednesday, 11 June, 13:30-17:30, Dorgelozaal
Support
Be prepared to share your data because it’s required and because you benefit from it Preparation = careful and responsible data management
Wrap up
Source: Research Data Netherlands / Marina Noordegraaf
Questions Do you experience data management problems within your work? Do you feel a need for more formal data management procedures / data
management planning within your work / laboratory? Would you consider publishing your research data sets, i.e. to make them
public available? Do you have reasons not to publish your research data? Are there privacy, legal or security issues involved with your data? Has someone ever asked for your research data?
Discussion questions
Source: Research Data Netherlands / Marina Noordegraaf
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Why? Storage problems? If storage problems, how are they solved? Or, because you collaborate with others? Findability of your own data? Getting the right version of your data? What happens with your data when your project is finished or when you leave the university? Are your data centrally stored? Are they described? What excuses do you have not to publish your research data?
![Research data management : [part of] PROOF course Finding and controlling scientific literature and data, Eindhoven University of Technology, 2015 / Leon Osinski](https://static.fdocuments.us/doc/165x107/559423511a28abfd568b462d/research-data-management-part-of-proof-course-finding-and-controlling-scientific-literature-and-data-eindhoven-university-of-technology-2015-leon-osinski.jpg)
