scholarly journals Promoting data sharing among Indonesian scientists: A proposal of generic university- level RDMP

2018 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Cut Novianti Rachmi
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Management Plan ◽  
Level Data ◽  
University Level

This article is basically a set of proposed checklists that can be used to draft a university-level data management plan (DMP). We will submit this manuscript to RioJournal (riojournal.com).

scholarly journals Promoting data sharing among Indonesian scientists: A proposal of generic university-level Research Data Management Plan (RDMP)

2018 ◽  
Vol 4 ◽  
pp. e28163
Author(s):  
Dasapta Irawan ◽  
Cut Rachmi
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
National Level ◽  
Management Plan ◽  
Research Data ◽  
Scientific Practices ◽  
Research Data Management ◽  
University Level ◽  
Time And Energy ◽  
Backup Storage

Every researcher needs data in their working ecosystem, but despite of the resources (funding, time, and energy), that they have spent to get the data, only a few are putting more real attention to data management. This paper is mainly describing our recommendation of RDMP document at university level. This paper would be a form of our initiative to be developed at university or national level, which also in-line with current development 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. Researcher can also develop more detail RDMP to cater specific project's environment. In this Research Data Management Plan (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 could increase researchers' awareness about data management to increase the value and impact of their researches.

scholarly journals Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing

2020 ◽  
Vol 15 (2) ◽  
pp. 168-170
Author(s):  
Jennifer Kaari
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Privacy ◽  
Management Practices ◽  
Open Data ◽  
Scientific Progress ◽  
Management Plan ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management

A Review of: Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196 Abstract Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing. Design – Email survey. Setting – Universities in Egypt, Jordan, and Saudi Arabia. Subjects – Surveys were sent to 4,086 academic faculty researchers. Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices. Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository.  Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities. Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.

scholarly journals PO 8495 DATA SHARING IS PART OF DATA MANAGEMENT: THE NEED FOR A HOLISTIC AND COHERENT VIEW ON RESEARCH DATA MANAGEMENT

2019 ◽  
Vol 4 (Suppl 3) ◽  
pp. A46.1-A46
Author(s):  
Harry Van Loen ◽  
Mary Thiongo ◽  
Yven Van Herrewege
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Qualitative Data ◽  
Management Plan ◽  
Data Management System ◽  
General Data Protection Regulation ◽  
General Data ◽  
The Cost ◽  
Holistic Vision ◽  
The Eu

BackgroundAwareness of data management (DM) is often restricted to ‘the cost of computers’ or ‘the need for a database’. Recently, ‘data sharing’ can be added to this shortlist. Indeed, in recent years data sharing became often required or so strongly promoted that the importance of all other aspects related to DM or data handling in clinical tended still to be overlooked. However, the development of data sharing guidelines and associated privacy regulations (e.g. the EU General Data Protection Regulation) created a new momentum for highlighting the importance of qualitative data management.MethodsAn overview of DM processes is given, within the framework and challenges of conducting non-commercial clinical trials in North-South partnerships.ResultsThe DM workflow of a clinical trial is presented, highlighting essential DM tasks, deliverables and milestones. Pre-study tasks and deliverables are addressed: SOPs, a data management plan, the implementation of a GCP-compliant validated data management system and compliance to data quality, privacy, security and standards (e.g. MedDRA, CDISC). Subsequent study-specific processes including the collection, entry, querying and cleaning of the data are discussed. In addition, DM metrics important to guide quality, productivity and timelines are reviewed while considering their impact on post-study activities such as data sharing.ConclusionData sharing is only one of many DM tasks, at the end of the DM workflow. Focusing too much on data sharing while neglecting other DM aspects might lead to underestimating the workload, resources, quality assurance and time needed for data management and by and large for the trial itself. Integrating data sharing into a holistic vision on data management is paramount for clinical research.

scholarly journals ITB Research Data Management Plan

2018 ◽  
Author(s):  
Dasapta Erwin Irawan
Keyword(s):  
Data Management ◽  
Management Plan ◽  
Research Data ◽  
Team Members ◽  
Research Data Management ◽  
Project Level

Here's the official ITB Research Data Management Plan. We use this plan as a template to design more detailed project-level RDMP. The document came from the work of ITB Repository Team that I lead. Team members: Sparisoma Viridi, Rino Mukti (I will add this list later). I invite everyone to re-use this document for their project-level RDMP.

scholarly journals TCR Data Management Plan

2021 ◽  
Author(s):  
Michael Russell ◽  
Vincent Paquit ◽  
Luke Scime ◽  
Alka Singh
Keyword(s):  
Data Management ◽  
Management Plan

scholarly journals Promoting Data Reuse and Collaboration at an Academic Medical Center

2015 ◽  
Vol 10 (1) ◽  
pp. 260-267 ◽  
Author(s):  
Kevin Read ◽  
Jessica Athens ◽  
Ian Lamb ◽  
Joey Nicholson ◽  
Sushan Chin ◽  
...  
Keyword(s):  
Data Management ◽  
Population Health ◽  
Data Sharing ◽  
Present State ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Reuse ◽  
Data Description ◽  
University Library ◽  
Academic Medical

A need was identified by the Department of Population Health (DPH) for an academic medical center to facilitate research using large, externally funded datasets. Barriers identified included difficulty in accessing and working with the datasets, and a lack of knowledge about institutional licenses. A need to facilitate sharing and reuse of datasets generated by researchers at the institution (internal datasets) was also recognized. The library partnered with a researcher in the DPH to create a catalog of external datasets, which provided detailed metadata and access instructions. The catalog listed researchers at the medical center and the main campus with expertise in using these external datasets in order to facilitate research and cross-campus collaboration. Data description standards were reviewed to create a set of metadata to facilitate access to both externally generated datasets, as well as the internally generated datasets that would constitute the next phase of development of the catalog. Interviews with a range of investigators at the institution identified DPH researchers as most interested in data sharing, therefore targeted outreach to this group was undertaken. Initial outreach resulted in additional external datasets being described, new local experts volunteering, proposals for additional functionality, and interest from researchers in inclusion of their internal datasets in the catalog. Despite limited outreach, the catalog has had ~250 unique page views in the three months since it went live. The establishment of the catalog also led to partnerships with the medical center’s data management core and the main university library. The Data Catalog in its present state serves a direct user need from the Department of Population Health to describe large, externally funded datasets. The library will use this initial strong community of users to expand the catalog and include internally generated research datasets. Future expansion plans will include working with DataCore and the main university library.

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