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* RDMP check lists or rubric (Digital Curation Center 2014<ref name="DCCChecklist">{{cite web |url=http://www.dcc.ac.uk/resources/data-management-plans/checklist |title=Checklist for a Data Management Plan |author=Digital Curation Center |date=2014}}</ref>, Teperek ''et al.'' 2017<ref name="TeperekWellcome17">{{cite web |url=https://zenodo.org/record/257650 |title=Wellcome DMP assessment rubric v2.0 |author=Teperek, M.; Mollitt, B.; Southall, J.; Donaldson, M. |work=Zenodo |date=23 January 2017 |doi=10.5281/zenodo.257650}}</ref>, University of California Curation Center 2018<ref name="UCCC_DMPTool">{{cite web |url=https://dmptool.org/ |title=DMPTool |author=University of California Curation Center |publisher=Regents of the University of California |date=2018}}</ref>)
* RDMP check lists or rubric (Digital Curation Center 2014<ref name="DCCChecklist">{{cite web |url=http://www.dcc.ac.uk/resources/data-management-plans/checklist |title=Checklist for a Data Management Plan |author=Digital Curation Center |date=2014}}</ref>, Teperek ''et al.'' 2017<ref name="TeperekWellcome17">{{cite web |url=https://zenodo.org/record/257650 |title=Wellcome DMP assessment rubric v2.0 |author=Teperek, M.; Mollitt, B.; Southall, J.; Donaldson, M. |work=Zenodo |date=23 January 2017 |doi=10.5281/zenodo.257650}}</ref>, University of California Curation Center 2018<ref name="UCCC_DMPTool">{{cite web |url=https://dmptool.org/ |title=DMPTool |author=University of California Curation Center |publisher=Regents of the University of California |date=2018}}</ref>)
* RDMP case study from various fields of sciences (Neylon 2017c<ref name="NeylonDataMan17">{{cite journal |title=Data Management Plan: IDRC Data Sharing Pilot Project |journal=Research Ideas and Outcomes |author=Neylon, C. |volume=3 |page=e14672 |year=2017 |doi=10.3897/rio.3.e14672}}</ref>, Traynor 2017<ref name="TraynorDataMan17">{{cite journal |title=Data Management Plan: Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights |journal=Research Ideas and Outcomes |author=Traynor, C. |volume=3 |page=e15111 |year=2017 |doi=10.3897/rio.3.e15111}}</ref>, Wael 2017<ref name="WaelDataMan17">{{cite journal |title=Data Management Plan: HarassMap |journal=Research Ideas and Outcomes |author=Wael, R. |volume=3 |page=e15133 |year=2017 |doi=10.3897/rio.3.e15133}}</ref>, Woolfrey 2017<ref name="WoolfreyDataMan17">{{cite journal |title=Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa |journal=Research Ideas and Outcomes |author=Woolfrey, L. |volume=3 |page=e14837 |year=2017 |doi=10.3897/rio.3.e14837}}</ref>)
* RDMP case study from various fields of sciences (Neylon 2017c<ref name="NeylonDataMan17">{{cite journal |title=Data Management Plan: IDRC Data Sharing Pilot Project |journal=Research Ideas and Outcomes |author=Neylon, C. |volume=3 |page=e14672 |year=2017 |doi=10.3897/rio.3.e14672}}</ref>, Traynor 2017<ref name="TraynorDataMan17">{{cite journal |title=Data Management Plan: Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights |journal=Research Ideas and Outcomes |author=Traynor, C. |volume=3 |page=e15111 |year=2017 |doi=10.3897/rio.3.e15111}}</ref>, Wael 2017<ref name="WaelDataMan17">{{cite journal |title=Data Management Plan: HarassMap |journal=Research Ideas and Outcomes |author=Wael, R. |volume=3 |page=e15133 |year=2017 |doi=10.3897/rio.3.e15133}}</ref>, Woolfrey 2017<ref name="WoolfreyDataMan17">{{cite journal |title=Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa |journal=Research Ideas and Outcomes |author=Woolfrey, L. |volume=3 |page=e14837 |year=2017 |doi=10.3897/rio.3.e14837}}</ref>)
==Component 1: Data collection==
===What types of data will you collect, create, link to, acquire and/or record?===
This RDMP covers the following type of data or documents, which are considered data sources:
* Raw data that may come in the following forms:
** any field or [[laboratory]] measurements collected during in a research
** any voice recording and its transcript of an interview or any other forms of data collection phase
** any vector and raster based images
** any video recording and its text caption of an interview or any other forms of data collection phase
** survey form responses from participants
** field notes or laboratory records
* Grant Proposals: funders may request researcher to submit their research plan as a pre-registration document in several platforms such as OSF or Curate Science
* Project-level RDMP: some funders, such as RCUK, mandate the submission of a final RDMP before the project begins
* Shared texts, voice, or video recordings of communication between team member
* Reports: may appear as a preliminary report, mid-term report, final report, or short communications
* Preprints: the preprint has been admitted as part of research output by several funders<ref name="BourneTenSimp17">{{cite journal |title=Ten simple rules to consider regarding preprint submission |journal=PLoS Computational Biology |author=Bourne, P.E.; Polka, J.K.; Vale, R.D.; Kiley, R. |volume=13 |issue=5 |page=e1005473 |year=2017 |doi=10.1371/journal.pcbi.1005473 |pmid=28472041 |pmc=PMC5417409}}</ref>
* Maps
===What file formats will your data be collected in? Will these formats allow for data re-use, sharing and long-term access to the data?===
Although most researchers use Microsoft-based applications, and most open repositories accept and provide a native viewer for many formats, the following are our choice of formats. You may refer to [https://library.sydney.edu.au/research/data-management/research-data-management-plans.html University of Sydney RDMP file formats] or [http://guides.library.cornell.edu/ecommons/formats Cornell University’s preservation file formats] for more information.
====Spreadsheets====
They should be written in text format, e.g., .csv (comma separated value) or .txt (using tab separated value). Data creators should format the spreadsheet in a "database" format by:
* starting the data immediately in cell (1,1);
* avoiding merging rows or columns; and
* clearly using the correct and consistent cell format, e.g., number, string, date, time, and category.
====Documents====
We recommend a text-based (ASCII) file, e.g., .txt, Markdown, or any other text format that can be created and read using a plain text reader like Notepad.
====Audio/video recordings====
* Audio recordings: .wav or .mp3
* Video recordings: .mp4 or .mpg
====Images and maps====
* General image: .jpg, .png, .bmp, .tiff
* Raster: geoTiff
* Vector: .shp
====Emails (project communications)====
Although most researchers are now using proprietary email clients like Microsoft Outlook or Apple Mail, they still need to store selected emails in plain text as well.
===What conventions and procedures will you use to structure, name and version control your files to help you and others better understand how your data are organized?===
Files are uploaded to an online repository and organized into folders by phase or by working package. If the file organization get too complicated to accommodate a set folder structure, then it should be separated and linked together. We recommend the following set of folders to organize the files.
root folder:
* data:
** raw
** processed
* analysis
** code (or script)
** tables
** figure or image
* output
** report
** presentation
** article (or manuscript)
Some field of research may have other specific folder arrangements, but generally they should have the components in the figure. If some team members choose to maintain a Google Drive, DropBox, Onedrive or other cloud service, then they should make an accessible link to the drives or folders and register the links to the data repository. To accommodate limited storage, the principal investigator (PI), co-PI, and team members may also maintain an open repository, such as OSF, Figshare, Zenodo, GitHub, GitLab, and other similar services, given that such services offer version control and access option features. All services should be linked together to a central repository. The team may also maintain a dedicated project website to store the data and related research documents, to keep track of the activities, and to store the project's repository or storage structure.


==References==
==References==

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Full article title Promoting data sharing among Indonesian scientists: A proposal of a generic university-level research data management plan (RDMP)
Journal Research Ideas and Outcomes
Author(s) Irawan, Dasapta E.; Rachmi, Cut N.
Author affiliation(s) Institut Teknologi Bandung, Universitas Padjadjaran
Primary contact Email: dasaptaerwin at outlook dot co dot id
Year published 2018
Volume and issue 4
Page(s) e28163
DOI 10.3897/rio.4.e28163
ISSN 2367-7163
Distribution license Creative Commons Attribution 4.0 International
Website https://riojournal.com/articles.php?id=28163
Download https://riojournal.com/article/28163/download/pdf/ (PDF)
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Every researcher needs data in their working ecosystem, but despite the resources (funding, time, and energy) they have spent to get the data, only a few are putting more real attention into data management. This paper mainly describes our recommendation of a research data management plan (RDMP) at the university level. This paper is an extension of our initiative, to be developed at the university or national level, while also in-line with current developments in scientific practices mandating data sharing and data re-use.

Researchers can use this article as an assessment form to describe the setting of their research and data management. Researchers can also develop a more detailed RDMP to cater to a specific project's environment. In this RDMP, we propose three levels of storage: offline working storage, offline backup storage, and online-cloud backup storage, located on a shared-repository. We also propose two kinds of cloud repository: a dynamic repository to store live data and a static repository to keep a copy of final data.

Hopefully, this RDMP could solve problems on data sharing and preservation, and additionally it could improve researchers' awareness about data management to increase the value and impact of their research efforts.

Keywords: research data management plan, open data, data sharing, data repository, reproducible research

Introduction

Good data management is capable of supporting scientific discovery[1], yet we have been observing a cultural barrier on data sharing.[2] More insights about data sharing and the diverse perceptions among scientists in various fields have been endlessly discussed.[3][4][5][6]

Every researcher needs data in their working ecosystem, but despite the resources (funding, time, and energy) they have spent to get the data, only a few are putting more real attention into data management.[7][8] A data management strategy is not just an administrative document; it also plays an important role in guiding researchers in storing, backing up, preserving, and sharing their research data in a proper and sustainable manner.

This paper describes a guideline to build a university-level research data management plan (RDMP) and how it can promote data sharing among scientists. This RDMP would be the first one to be developed at the university level in Indonesia. This project is in-line with current development in scientific practices mandating data sharing and data re-use. The goals of this RDMP project are to build awareness about data sharing and preservation to scientists, especially academic staffs, and to build a practical and simple tool to help them manage their research data. The goal of an RDMP project is to guide researchers in managing their data, including curating, storing, sharing, and preserving it for immediate and future use.

This RDMP proposal is largely extracted from our experience in developing RDMP for an international research collaboration funded by RCUK (Research Council UK).[9]

Description

General overview

The concern to having a proper RDMP was triggered by difficulties faced by researchers to find data from another researcher or previous research and to extract data from reports. The other problem is to find guidelines, especially in Indonesia, on how to appropriately manage your research data, to store them, and to keep them available in the long run. Clearly scientists have issues on how to re-use datasets from prior research, how to cite them in their own work (re-use), and how to know the limitation of such actions.

Due to the large effort to get data in terms of funding, time, and energy, the longevity of data should be more than one or two years, as we find to be the general case in the Indonesia research ecosystem (Fig. 1).[9][10][11][12] Another important point to address is the barrier of data sharing that involves the fear of getting scooped, the lack of knowledge concerning intellectual property rights (IPR), and data ownership. Therefore, by developing this document, we could solve the barriers and at the same time we could come up with another way to increase the value of research data, instead of only looking at mainstream metrics.


Fig1 Irawan ResIdeasOut2018 4.png

Figure 1. Current situation of data lifecycle

How to use this article as a set of guidelines

Researchers can use this article as an assessment form to describe the setting of their research and data management requirements from a potential funder. Researchers can also develop a more detailed RDMP to cater to a specific project's environment. They should justify the setting of their research and requirement of the funder regarding data sharing and data preservation.

Seven components in RDMP

The proposed RDMP is divided into seven components:

  1. Data collection
  2. Documentation and metadata
  3. Storage and backup
  4. Preservation
  5. Sharing and re-use
  6. Responsibilities and resources
  7. Ethics and legal compliance

References

Given the different nature of research, funders, and DMP standards, we refer to the following sources in developing this RDMP:

  • Data sharing culture (Neylon 2017a[10], Neylon 2017b[11])
  • Open data principles and reproducible research (Irawan et al. 2017[13])
  • RDMP check lists or rubric (Digital Curation Center 2014[14], Teperek et al. 2017[15], University of California Curation Center 2018[16])
  • RDMP case study from various fields of sciences (Neylon 2017c[17], Traynor 2017[18], Wael 2017[19], Woolfrey 2017[20])

Component 1: Data collection

What types of data will you collect, create, link to, acquire and/or record?

This RDMP covers the following type of data or documents, which are considered data sources:

  • Raw data that may come in the following forms:
    • any field or laboratory measurements collected during in a research
    • any voice recording and its transcript of an interview or any other forms of data collection phase
    • any vector and raster based images
    • any video recording and its text caption of an interview or any other forms of data collection phase
    • survey form responses from participants
    • field notes or laboratory records
  • Grant Proposals: funders may request researcher to submit their research plan as a pre-registration document in several platforms such as OSF or Curate Science
  • Project-level RDMP: some funders, such as RCUK, mandate the submission of a final RDMP before the project begins
  • Shared texts, voice, or video recordings of communication between team member
  • Reports: may appear as a preliminary report, mid-term report, final report, or short communications
  • Preprints: the preprint has been admitted as part of research output by several funders[21]
  • Maps

What file formats will your data be collected in? Will these formats allow for data re-use, sharing and long-term access to the data?

Although most researchers use Microsoft-based applications, and most open repositories accept and provide a native viewer for many formats, the following are our choice of formats. You may refer to University of Sydney RDMP file formats or Cornell University’s preservation file formats for more information.

Spreadsheets

They should be written in text format, e.g., .csv (comma separated value) or .txt (using tab separated value). Data creators should format the spreadsheet in a "database" format by:

  • starting the data immediately in cell (1,1);
  • avoiding merging rows or columns; and
  • clearly using the correct and consistent cell format, e.g., number, string, date, time, and category.

Documents

We recommend a text-based (ASCII) file, e.g., .txt, Markdown, or any other text format that can be created and read using a plain text reader like Notepad.

Audio/video recordings

  • Audio recordings: .wav or .mp3
  • Video recordings: .mp4 or .mpg

Images and maps

  • General image: .jpg, .png, .bmp, .tiff
  • Raster: geoTiff
  • Vector: .shp

Emails (project communications)

Although most researchers are now using proprietary email clients like Microsoft Outlook or Apple Mail, they still need to store selected emails in plain text as well.

What conventions and procedures will you use to structure, name and version control your files to help you and others better understand how your data are organized?

Files are uploaded to an online repository and organized into folders by phase or by working package. If the file organization get too complicated to accommodate a set folder structure, then it should be separated and linked together. We recommend the following set of folders to organize the files.

root folder:

  • data:
    • raw
    • processed
  • analysis
    • code (or script)
    • tables
    • figure or image
  • output
    • report
    • presentation
    • article (or manuscript)

Some field of research may have other specific folder arrangements, but generally they should have the components in the figure. If some team members choose to maintain a Google Drive, DropBox, Onedrive or other cloud service, then they should make an accessible link to the drives or folders and register the links to the data repository. To accommodate limited storage, the principal investigator (PI), co-PI, and team members may also maintain an open repository, such as OSF, Figshare, Zenodo, GitHub, GitLab, and other similar services, given that such services offer version control and access option features. All services should be linked together to a central repository. The team may also maintain a dedicated project website to store the data and related research documents, to keep track of the activities, and to store the project's repository or storage structure.

References

  1. ↑ Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J. et al. (2016). "The FAIR Guiding Principles for scientific data management and stewardship". Scientific Data 3: 160018. doi:10.1038/sdata.2016.18. 
  2. ↑ Davidson, J.; Jones, S.; Molloy, L. et al. (2014). "Emerging Good Practice in Managing Research Data and Research Information within UK Universities". Procedia Computer Science 33: 215–22. doi:10.1016/j.procs.2014.06.035. 
  3. ↑ Tenopir, C.; Allard, S.; Douglass, K. et al. (2011). "Data sharing by scientists: Practices and perceptions". PLoS One 6 (6): e21101. doi:10.1371/journal.pone.0021101. PMC PMC3126798. PMID 21738610. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3126798. 
  4. ↑ Tenopir, C.; Dalton, E.D.; Allard, S. et al. (2015). "Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide". PLoS One 10 (8): e0134826. doi:10.1371/journal.pone.0134826. PMC PMC4550246. PMID 26308551. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4550246. 
  5. ↑ van Panhuis, W.G.; Paul, P.; Emerson, C. et al. (2014). "A systematic review of barriers to data sharing in public health". BMC Public Health 14: 1144. doi:10.1186/1471-2458-14-1144. PMC PMC4239377. PMID 25377061. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239377. 
  6. ↑ Wallis, J.C.; Rolando, E.; Borgman, C.L. (2013). "If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology". PLoS One 8 (7): e67332. doi:10.1371/journal.pone.0067332. PMC PMC3720779. PMID 23935830. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720779. 
  7. ↑ Irawan, D.E. (24 April 2018). "RDM policy and data archiving at university level -- technical bits -- an example from ITB". Figshare. https://figshare.com/articles/RDM_policy_and_data_archiving_at_university_level_--_technical_bits_--_an_example_from_ITB/6179084/1. 
  8. ↑ Irawan, D.E. (19 September 2017). "A light introduction to research data management". Figshare. https://figshare.com/articles/A_light_introduction_to_research_data_management/5418694/1. 
  9. ↑ 9.0 9.1 Irawan, D.E.; Rachmi, C.N. (15 May 2018). "Promoting data sharing among Indonesian scientists: A proposal of generic university-level RDMP". Open Science Framework. doi:10.17605/OSF.IO/59VCN. https://osf.io/59vcn/. 
  10. ↑ 10.0 10.1 Neylon, C. (2017). "Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers". Research Ideas and Outcomes 3: e14673. doi:10.3897/rio.3.e14673. 
  11. ↑ 11.0 11.1 Neylon, C. (2017). "Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research". Research Ideas and Outcomes 3: e21773. doi:10.3897/rio.3.e21773. 
  12. ↑ Neylon, C. (2017). "Support Your Data: A Research Data Management Guide for Researchers". Research Ideas and Outcomes 4: e26439. doi:10.3897/rio.4.e26439. 
  13. ↑ Irawan, D.E.; Vervoort, R.W.; Melzack, G. (19 December 2017). "Open Data Workshop SSEAC Usyd - ITB". Open Science Framework. doi:10.17605/OSF.IO/S76GU. https://osf.io/s76gu/. 
  14. ↑ Digital Curation Center (2014). "Checklist for a Data Management Plan". http://www.dcc.ac.uk/resources/data-management-plans/checklist. 
  15. ↑ Teperek, M.; Mollitt, B.; Southall, J.; Donaldson, M. (23 January 2017). "Wellcome DMP assessment rubric v2.0". Zenodo. doi:10.5281/zenodo.257650. https://zenodo.org/record/257650. 
  16. ↑ University of California Curation Center (2018). "DMPTool". Regents of the University of California. https://dmptool.org/. 
  17. ↑ Neylon, C. (2017). "Data Management Plan: IDRC Data Sharing Pilot Project". Research Ideas and Outcomes 3: e14672. doi:10.3897/rio.3.e14672. 
  18. ↑ Traynor, C. (2017). "Data Management Plan: Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights". Research Ideas and Outcomes 3: e15111. doi:10.3897/rio.3.e15111. 
  19. ↑ Wael, R. (2017). "Data Management Plan: HarassMap". Research Ideas and Outcomes 3: e15133. doi:10.3897/rio.3.e15133. 
  20. ↑ Woolfrey, L. (2017). "Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa". Research Ideas and Outcomes 3: e14837. doi:10.3897/rio.3.e14837. 
  21. ↑ Bourne, P.E.; Polka, J.K.; Vale, R.D.; Kiley, R. (2017). "Ten simple rules to consider regarding preprint submission". PLoS Computational Biology 13 (5): e1005473. doi:10.1371/journal.pcbi.1005473. PMC PMC5417409. PMID 28472041. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417409. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, and grammar for improved readability. In some cases important information was missing from the references, and that information was added. The original article listed citations in alphabetical order, while this wiki lists them by order of appearance, by design.

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