1. Research data management during and after your research an introductionWorkshop PhD students department of Mechanical Engineering 31-01-2013Leon Osinski en Maurits van der Graaf TU/e, IEC / Bibliotheek Beschikbaar onder een CC-BY licentie

2. Workshop subject and objectives Subject Research data management, or how to deal carefully and honestly with research data 1. Honestly: research data management in connection with scientific integrity and questionable practices 2. Carefully: research data management during your research and after your researchObjectives 1.2.Providing insight into the why of research data management during and after your research; To gain insight into existing data management practices and needs of PhD students 3. Workshop program Program 13:30-13:35: introduction 13:35-14:00: presentation Scientific integrity and research data management during your research 14:00-14:30: discussion 14:30-14:35: break 14:35-15:00: presentation Research data management after your research 15:00-15:30: discussion 15:30-15:40: closure and final discussion topics 4. Presentation 1 SCIENTIFIC INTEGRITY AND RESEARCH DATA MANAGAMENT DURING RESEARCH 5. Scientific integrity and questionable practices Deliberately fabricating research data (Schn, Stapel, Fujii) Falsificating research data Adjusting research data Omitting or eliminating unpleasant data, incomplete reporting of results, not mentioning failed experiments (verification bias), partial reporting of data (cherry picking) Plagiarism: using data from someone else without mentioning the source 6. Scientific integrity & research data management research data management to ensure research integrity and replicationPrinciple Presented information is verifiable. Whenever research results are publicized, it is made clear what the data and the conclusions are based on, where they were derived from and how they can be verified.Best Practice Research must be replicable in order to verify its accuracy. The choice of research question, the research set-up, the choice of method and the reference to sources studied is accurately documented. The quality of data collection, data input, data storage and data processing is guarded closely. All steps taken must be properly reported and their execution must be properly monitored (lab journals, progress reports, documentation of arrangements and decisions, etc.). Raw research data are stored for at least five years. These data are made available to other scientific practitioners at request. Raw research data are archived in such a way that they can be consulted at a minimum expense of time and effort. From: VSNU, The Netherlands code of conduct for scientific practice: principles of good scientific teaching and research (VSNU, 2012), p. 7 >Research results must be verifiable research should be replicable data collection, storage and processing should be reported and monitored data management Untraceable research data: Poldermans denies data fabrication but cannot prove his right because data are lost 7. Research data management research in progressExcept in view of scientific integrity, during the course of a research project there will inevitable be a moment when one wishes to store data safely and accessible for others: for further use, processing and analysis within ones own research group for use in courses for exchange with researchers elsewhere who collaborate in the project for assessment by supervisor or tutor 8. Research data management research in progressWhy manage your data during your research? To ensure your research data are accurate, complete, authentic and reliable To enhance data security and minimise the risk of data loss To enable others to access your data: sharing data implies data management To increase your research efficiency: reuse of your data by yourselfRequirements Storage (space, equipment) Metadata: data documentation in view of transparancy Controlled access: who will have access to the data? [ Dataverse Network: http://thedata.org ; http://www.dataverse.nl/dvn/ ] 9. Discussion 1 SCIENTIFIC INTEGRITY AND RESEARCH DATA MANAGEMENT DURING RESEARCH 10. Research data management discussion topics and questions 1Questions with regard to scientific integrity What do you do with data sets from failed experiments or wrong measurements? What criteria are there for establishing failure? Are all data sets used for publications? What happens to data sets that arent used?Questions with regard to the nature of research data What is the type of your research data? Quantitative measurement data (obtained from lab equipment) Observational data (sensor data, neuroimages, survey data) Simulation (data derived from test models) What is your research data format? Text: txt, Word, PDF, XML Numerical: Excel, SPSS, R Multimedia: jpeg, tiff, mpeg, quicktime, vol (3D images) Software: Java, C, Matlab, Mathematica Discipline specific Instrument specific: MRC (cryon-electron micoscropy) 11. Research data management discussion topics and questions 1Questions with regard to the research context of your data Own, self-generated data or (also) data produced by others? Are data produced as part of a collaboration or in the context of an individual project?Questions with regard to the management of data Estimated volume of data? Sufficient data storage space? Whos responsible for storage and back up of your data? Yourself, someone else? Who owns or controls your data? Yourself, principal investigator, data manager? Who decides on access to the data? Are any data lost and if so is that a problem? Are data selections made? Not everything is to be stored but? 12. Research data management discussion topics and questions 1Questions with regard to accessibility of data during your research Are data accessible to others and if yes, to whom and how? Are data requested by persons from outside ones own research group? Are there any special privacy or security requirements with regard to your data? Are data documented or described? Are standards used for that? 13. Presentation 2 RESEARCH DATA MANAGEMENT AFTER YOUR RESEARCH 14. Research data management after your researchResearch data management after your research is primarily a matter of archiving or publishing your data Why archiving your data after your research? Because data providing the evidence for a published paper can be asked for by others in view of verificating or replicating your results (scientific integrity) Because journal, funder or code of conduct demand data to be accessible Because data are unique and / or valuable (non-repeatable observations) Because data are an asset, worth sharing in order to be reused or built on by others UPSIDE: Uniform Principle of Sharing Integral Data and Materials Expeditiously 15. Research data management after your researchWhy making your data public (publishing your data)? Data are an asset, worth sharing in order to be reused or built on by others Data-driven science: building on existing data or combining data to move science forward or to generate new science (Fourth paradigm of science) 16. Research data management after your researchThe best way to share your data is to publish your data with a DOI [digital object identifier] Making your data public with a DOI allows you to claim your data retrieve your data cite your data enhance your publication by integrating data with your paper 17. Research data management after your researchDocumentation (metadata) If you want to publish your data, mere availability of data has no value. To be useable, data need to be well-documented Data should be made public in an 1accessible, 2intelligible, 3assessable and 4reusable way intelligent openness of data 18. Research data management after your researchWhere to publish your data? 1. Repositories / archives: 3TU.Datacentrum DANS PANGEA Figshare UCI Machine Learning Repository 2. Data journals 19. Discussion 2 RESEARCH DATA MANAGEMENT AFTER YOUR RESEARCH 20. Research data management discussion topics and questions 2Questions What happens with your data when your project is finished or when you leave the university? Who owns your data when your project is finished? Are data at the end of a project centrally stored? How do you ensure your data are easy to understand or analyse? Do you use metadata standards to describe your data? 21. Research data management discussion topics and questions 2Questions Are there any journals within ones discipline who require data to be made available? Are there research funders within ones discipline who require data to be made available? Are there examples within ones discipline of stand-alone data publications? Are there reasons not to publish your data, i.e. to make them public? Are there privacy, legal or security issues involved with your data? Has someone ever asked for your research data? 22. Research data management final discussion topics Do you feel a need for more formal data management procedures / data management plan within your laboratory? Would you consider publishing your research data sets?