scholarly journals Differences in data-sharing attitudes and behaviours, extended version to African data curators and data management experts

2021 ◽  
Vol 45 (3-4) ◽  
Author(s):  
Flavio Bonifacio ◽  
Winny Nekesa Akullo
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Previous Article ◽  
Extended Version ◽  
New Findings ◽  
Regional Workshop

This article reports the results of a survey conducted between 16th November and 8th December 2020 among African data curators and data experts about different aspects of data sharing. The sample of respondents has been extracted from participants to the 1st IASSIST Africa Regional Workshop held on 11th -13th January 2021, Kampala, UGANDA and other data experts and practitioners. First, we recall the main results of a previous article published by IQ about the same argument in order to introduce the new survey. After that we analyse the new findings comparing them with the previous results, splitting the samples between Africans and not Africans.

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.

scholarly journals A Reliable and Distributed LIMS for Efficient Management of the Microarray Experiment Environment

2007 ◽  
Vol 4 (1) ◽  
pp. 115-131
Author(s):  
Hee-Jeong Jin ◽  
Jeong-Won Lee ◽  
Hwan-Gue Cho
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Service Provider ◽  
Expression Profiles ◽  
Microarray Experiment ◽  
Genetic Network ◽  
Information Management System ◽  
Project Schedule ◽  
Joint Research ◽  
Shared Data

Summary A microarray is a principal technology in molecular biology. It generates thousands of expressions of genotypes at once. Typically, a microarray experiment contains many kinds of information, such as gene names, sequences, expression profiles, scanned images, and annotation. So, the organization and analysis of vast amounts of data are required. Microarray LIMS (Laboratory Information Management System) provides data management, search, and basic analysis. Recently, microarray joint researches, such as the skeletal system disease and anti-cancer medicine have been widely conducted. This research requires data sharing among laboratories within the joint research group. In this paper, we introduce a web based microarray LIMS, SMILE (Small and solid MIcroarray Lims for Experimenters), especially for shared data management. The data sharing function of SMILE is based on Friend-to-Friend (F2F), which is based on anonymous P2P (Peer-to-Peer), in which people connect directly with their “friends”. It only allows its friends to exchange data directly using IP addresses or digital signatures you trust. In SMILE, there are two types of friends: “service provider”, which provides data, and “client”, which is provided with data. So, the service provider provides shared data only to its clients. SMILE provides useful functions for microarray experiments, such as variant data management, image analysis, normalization, system management, project schedule management, and shared data management. Moreover, it connections with two systems: ArrayMall for analyzing microarray images and GENAW for constructing a genetic network. SMILE is available on http://neobio.cs.pusan.ac.kr:8080/smile.

scholarly journals MARDI rice genebank: important roles in data management and data sharing

2020 ◽  
Vol 482 ◽  
pp. 012013
Author(s):  
S Noorzuraini ◽  
M Shukri ◽  
A Amron ◽  
M Izzat ◽  
M Ramdzan ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing

scholarly journals Intelligent Data Management and Security in Cloud Computing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3458
Author(s):  
Lidia Ogiela ◽  
Marek R. Ogiela ◽  
Hoon Ko
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Service Management ◽  
Secret Data ◽  
Data Services ◽  
New Class ◽  
Management Processes ◽  
Confidential Data ◽  
Shared Secret ◽  
Linguistic Methods

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

Relational data sharing in peer-based data management systems

2003 ◽  
Vol 32 (3) ◽  
pp. 59-64 ◽  
Author(s):  
Beng Chin Ooi ◽  
Yanfeng Shu ◽  
Kian-Lee Tan
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Relational Data ◽  
Management Systems ◽  
Data Management Systems

scholarly journals Perceptions and Attitudes toward Data Sharing among Dental Researchers

2018 ◽  
Vol 4 (1) ◽  
pp. 68-75 ◽  
Author(s):  
H. Spallek ◽  
S.M. Weinberg ◽  
M. Manz ◽  
S. Nanayakkara ◽  
X. Zhou ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
International Association ◽  
Improve Patient Care ◽  
Research Data ◽  
Research Subjects ◽  
Data Repositories ◽  
Use Of Data ◽  
Share Data ◽  
Past Experiences

Introduction: Increasing attention is being given to the roles of data management and data sharing in the advancement of research. This study was undertaken to explore opinions and past experiences of established dental researchers as related to data sharing and data management. Methods: Researchers were recruited from the International Association for Dental Research scientific groups to complete a survey consisting of Likert-type, multiple-choice, and open-ended questions. Results: All 42 respondents indicated that data sharing should be promoted and facilitated, but many indicated reservations or concerns about the proper use of data and the protection of research subjects. Many had used data from data repositories and received requests for data originating from their studies. Opinions varied regarding restrictions such as requirements to share data and the time limits of investigator rights to keep data. Respondents also varied in their methods of data management and storage, with younger respondents and those with higher direct costs of their research tending to use dedicated experts to manage their data. Discussion: The expressed respondent support for research data sharing, with the noted concerns, complements the idea of developing managed data clearinghouses capable of promoting, managing, and overseeing the data-sharing process. Knowledge Transfer Statement: Researchers can use the results of this study to evaluate and improve management and sharing of research data. By encouraging and facilitating the data-sharing process, research can advance more efficiently, and research findings can be implemented into practice more rapidly to improve patient care and the overall oral health of populations.

SUPPORTING MULTI-SYNCHRONOUS GROUPWARE: DATA MANAGEMENT PROBLEMS AND A SOLUTION

2006 ◽  
Vol 15 (02) ◽  
pp. 229-258 ◽  
Author(s):  
NUNO PREGUIÇA ◽  
J. LEGATHEAUX MARTINS ◽  
HENRIQUE JOÃO DOMINGOS ◽  
SÉRGIO DUARTE
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Management System ◽  
Work Practice ◽  
Data Management System ◽  
Collaborative Activity ◽  
Activity Groups ◽  
Automatic Mechanism ◽  
Collaborative Activities

It is common that, in a long-term asynchronous collaborative activity, groups of users engage in occasional synchronous sessions. In this paper, we analyze the data management requirements for supporting this common work practice in typical collaborative activities and applications. We call the applications that support such work practice multi-synchronous applications. This analysis shows that, as users interact in different ways in each setting, some applications have different requirements and need to rely on different data sharing techniques in synchronous and asynchronous settings. We present a data management system that allows to integrate a synchronous session in the context of a long-term asynchronous interaction, using the suitable data sharing techniques in each setting and an automatic mechanism to convert the long sequence of small updates produced in a synchronous session into a large asynchronous contribution. We exemplify the use of our approach with two multi-synchronous applications.

scholarly journals How does DICOM support big data management? Investigating its use in medical imaging community

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Marco Aiello ◽  
Giuseppina Esposito ◽  
Giulio Pagliari ◽  
Pasquale Borrelli ◽  
Valentina Brancato ◽  
...  
Keyword(s):  
Big Data ◽  
Medical Imaging ◽  
Data Management ◽  
Data Sharing ◽  
Data Storage ◽  
Big Data Analytics ◽  
Software Tools ◽  
Imaging Data ◽  
Privacy Concerns ◽  
Reading And Writing

AbstractThe diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

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