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Research Data Management Plan (RDMP)

The data management plan is the official document that sets out how every scientist of the Zernike Institute for Advanced Materials must deal with research data during the research and once the research project has been completed. Drawing up an RDMP before the data is collected ensures that the data is generated in the correct format and is categorized properly. At the Zernike Institute for Advanced Materials research data needs to be properly archived for purposes of both verification (safeguarding scientific integrity) and safekeeping of valuable datasets. The structure of the Zernike Institute RDMP aims for implementation in an easy and logical way. It builds on well-established practices common for the labs of the Zernike Institute, while following the recommendations of NWO, KNAW and the RuG - Faculty of Science and Engineering.

Definition of data

Because of the multidisciplinary nature of the research within the institute, research data may vary. They can be observational data, experimental data, simulation data from models or processed data. Data types could include text, numbers, images, 3D models, software, audio files, video files, reports, etc.. Each researcher (or research group) will need to decide per research project what type of data will be collected and stored and what file format will be the most appropriate for storage. Obviously, data generated by researchers of the Zernike Institute for Advanced Materials need to meet the criteria defined by the Board of the University of Groningen:

- accurate, complete, reliable, authentic and provided with metadata (text file describing the data sources in relation to (corresponding sections of the document);

- safely stored with minimum risk of loss and for at least 10 years;

- be available for review and further study after completion of the research and / or departure of the researcher.

Verifiability

Verifiability of data is a must. Published results clearly have to show:

a) upon what the data and the conclusions are based;

b) how they are derived;

c) where and how they can be verified.

This means that all those involved in data collection and management will need to meet the standards of good data management and will have to act according to the procedures described in this protocol. This means that each researcher will need to decide per research project and based on the nature of the research what type of data will be collected and stored, and what file format will be the most appropriate for storage.

The Zernike Institute for Advanced Materials coordinating office will advise the director and the scientific staff of the institute on data management plans, storage questions and eventual destruction of research data.

The RDMPs of the Zernike Institute for Advanced Materials will be linked to the R&O interviews.

Research Data Management Agreements

Responsibilities

• It is the responsibility of the individual Zernike Institute for Advanced Materials researcher/PI to design a project research data management plan according to the template and act accordingly. An important part of this protocol is a plan for access, reuse and storage of data at the end of the study.

• The scientific director ensures that the agreements described in this document are strictly being followed.

Who needs to act

• The Zernike Institute for Advanced Materials provides a template RDMP to be used by each researcher for each research project. As research in the institute is multidisciplinary it might be too diverse to be covered by one uniform protocol. Therefore, the template RDMP might be adjusted or extended to meet additional criteria (Please always use the latest version available at https://rdmp.webhosting.rug.nl).

• Every researcher (Master’s student, PhD candidate, postdoc and permanent staff) will fill out a RDMP for each research project he/she is involved in and will do this before or upon the actual start of the project. A new version of the RDMP should be created whenever important changes to the project occur due to inclusion of new data sets, changes in consortium policies or external factors.

• PhD candidates will add the filled RDMP to their Training and Support Plan (TSP) as an attachment, not later than 6 month after the start of the project. Master students will add the RDMP as an appendix to their master project report. Other staff stores the different RDMPs in a personal file at the secretariat of the research unit.

• RDMPs and the associated raw and processed data are available upon request to the chair of the research group and the director of the institute.

What, where and when to store

• All data (raw, preliminary data and secondary, processed data) underlying an intended publication will be archived together with the final publication. In addition a file is added containing information on how all these relate to the document, e.g. describe which data files were used to plot a particular figure, or which parameters were used to generate the simulated datasets used for the publication.

In addition, experimental groups will store all lab journals (digital as well as paper ones).

• Digital raw and processed data is at least stored on the research group’s Y-drive. If better long-term storage solutions become available this paragraph might be adjusted.

• Paper lab journals are stored in a safe cabinet at the research group.

Data has to be deposited according to the following guidelines:

• For intended publications in any form, each MSc, PhD student, scientific staff member should compile a documented archive of all data underlying a publication and store it on the research groups Y-drive within 3 months after the paper appears online, including publication ‘early online’.

• For data collected in the context of an MSc study: All data should be deposited no later than the date of upon handing in the final version of the MSc-report. A grade will only be awarded when all data are deposited in agreement with the daily supervisor of the project (PhD student, postdoc, staff member etc.). The daily supervisor should deposit the data on the Y-drive no longer than 1 month after the grade has been awarded to the student.

• For data collected in the context of a PhD study: The documented data archive of the study should be deposited in agreement with the supervisor (promotor) upon deposition of the final manuscript in HoraFinita. The supervisor (promotor) will only sign the approval form of the PhD thesis when the data archive of the study has been handed in. Also parts/chapters that have already undergone a previous storage procedure upon publication of the paper are to be included in the PhD study archive as one of the folders for each chapter.

• Data that is collected and stored at an external institute, falls under the responsibility of the external institute and does not need to be deposited on the Y-drive. However, this is ONLY the case for raw, primary data. All processed, secondary data such as spreadsheets, databases, scripts, code etc. that is used for the thesis/publication must be saved on the Y-drive of the research group.

• Large primary datasets that are stored elsewhere in a public database do not need to be deposited on the Y-drive. However, all secondary data must be saved.

• The Zernike Institute for Advanced Materials research units act with full responsibility and under all circumstances for the data produced within the research unit and its data management.

• All data, including lab journals, are stored for at least 10 years after publication of the manuscript.

Additional information on safe data storage and a list of file formats

Below you find additional information on safe data storage and a list of file formats, which allow processing with standard software to prevent data loss in case specialized soft- or hardware becomes obsolete. Further, this section provides a guideline for naming of digital files, such that they remain useful even years (decades) after departure of a scientist.

Data Storage and Back-up Guide

Generally there are four options for data storage:

Networked drives: University fileserver –As these are secure and backed-up regularly, they are ideal for master copies of your research data.

Local drives: PCs and Laptops – Data can be lost because local drives can fail, or the computer may be lost or stolen. These are convenient for short-term storage and data processing but should not be relied upon for storing master copies, unless backed-up regularly.

Remote or Cloud storage – commonly used services, such as Dropbox and Google Drive, will not be appropriate for sensitive data, and their service level agreements should be studied before using them to store your research data.

External portable storage devices – External hard drives, USB drives, DVDs and CDs. These are very convenient, being cheap and portable, but not recommended for long-term storage as their longevity is uncertain and they can be easily damaged.

File formats guide

The file formats below are recommendations based on evaluation performed by DANS. The preferred formats are very likely to remain software accessible even in years from now. Nevertheless, it is very likely, that new file formats will appear, which might be even more robust than the mentioned ones.

Data type

Preferred format(s)

Acceptable format(s)

Text documents

* PDF/A (.pdf)

* OpenDocument Text (.odt)

* MS Word (.doc, .docx)

* Rich Text File (.rtf)

* PDF (.pdf)

Plain text

* Unicode TXT (.txt, ...)

* Non-Unicode TXT (.txt, ...)

Spreadsheets

* PDF/A (.pdf)

* Comma Separated Values (.csv)

* OpenDocument Spreadsheet (.ods)

* MS Excel (.xls, .xlsx)

Databases

* ANSI SQL (.sql, …)

* Comma Separated Values (.csv)

* MS Access (.mdb, .accdb)

* dBase III or IV (.dbf)

Statistical data

* SPSS Portable (.por)

* SAS transport (.sas)

* STATA (.dta)

Figures (rasterized)

* JPEG (.jpg, .jpeg)

* TIFF (.tif, .tiff)

Figure (vector)

* PDF/A (.pdf)

* Scalable Vector Graphics (.svg)

* Adobe Illustrator (.ai)

* PostScript (.eps)

* PDF (.pdf)

Video

* MPEG-2 (.mpg, .mpeg, …)

* MPEG-4 H264 (.mp4)

* Lossless AVI (.avi)

* QuickTime (.mov)

Audio

* WAVE (.wav)

* MP3 AAC (.mp3)

Computer Aided Design

* AutoCAD DXF versie R12 (.dxf)

* AutoCAD andere versies (.dwg, .dxf)

Data naming recommendations

The naming of data is a crucial part in the process of data management. Naming the files with a good logic allows for easy identification of ownership. Further, it enables the fast connection of a given datafile to the respective documentation.

Examples:     Minimalistic:                       Name_yyyymmdd_Keyword

                                                          Name_Initials_yyyymmdd_Keyword

                      Extended:                 FamilyName_GivenName_yyyymmdd_Keyword

You can also include the nature of the datafile by adding _raw or _processed at the end.

While the naming structure can be rather simple, it should allow for identification of the data owner even when he or she has a very common name. Therefore an extension of the structure by the abbreviation of the research group might be of added value.

Example:                                           ResearchGroup_Name_Initials_yyyymmdd_Keyword

We strongly recommend NOT to use your p- or s- number since it can be reassigned to other employees/ students after your relationship with the RUG is terminated.

Help?

Contact your institute's RDMP coordinator!

Last modified:02 February 2017 09.14 a.m.