scholarly journals Between administration and research - Understanding data management practices in a mid-sized technical university

2020 ◽  
Author(s):  
Stefan Reichmann ◽  
Thomas Klebel ◽  
Ilire Hasani-Mavriqi ◽  
Tony Ross-Hellauer
Keyword(s):  
Data Management ◽  
Management Practices ◽  
The State ◽  
Data Sets ◽  
Data Archiving ◽  
Research Institution ◽  
University Of Technology ◽  
Data Collection And Analysis ◽  
Administrative Task ◽  
Good Research

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents the results of an institutional survey (N=258) at Graz University of Technology, supplemented with interview data (N=18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

scholarly journals Practices and Infrastructures for ML Systems – An Interview Study

2021 ◽  
Author(s):  
Dennis Muiruri ◽  
Lucy Ellen Lwakatare ◽  
Jukka K. Nurminen ◽  
Tommi Mikkonen
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Interview Study ◽  
The State ◽  
Data Driven ◽  
Software Systems ◽  
State Of Practice ◽  
Model Training

<div> <div> <div> <p>The best practices and infrastructures for developing and maintaining machine learning (ML) enabled software systems are often reported by large and experienced data-driven organizations. However, little is known about the state of practice across other organizations. Using interviews, we investigated practices and tool-chains for ML-enabled systems from 16 organizations in various domains. Our study makes three broad observations related to data management practices, monitoring practices and automation practices in ML model training, and serving workflows. These have limited number of generic practices and tools applicable across organizations in different domains. </p> </div> </div> </div>

scholarly journals A survey of research quality in core facilities

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Isabelle C Kos-Braun ◽  
Björn Gerlach ◽  
Claudia Pitzer
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Process ◽  
Research Quality ◽  
Research Practice ◽  
Experimental Equipment ◽  
Research Practices ◽  
Core Facilities ◽  
First Contact ◽  
Good Research

Core facilities are an effective way of making expensive experimental equipment available to a large number of researchers, and are thus well placed to contribute to efforts to promote good research practices. Here we report the results of a survey that asked core facilities in Europe about their approaches to the promotion of good research practices, and about their interactions with users from the first contact to the publication of the results. Based on 253 responses we identified four ways that good research practices could be encouraged: (i) motivating users to follow the advice and procedures for best research practice; (ii) providing clear guidance on data-management practices; (iii) improving communication along the whole research process; and (iv) clearly defining the responsibilities of each party.

scholarly journals Modifying researchers’ data management practices

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 253-265 ◽  
Author(s):  
Susan Hickson ◽  
Kylie Ann Poulton ◽  
Maria Connor ◽  
Joanna Richardson ◽  
Malcolm Wolski
Keyword(s):  
Data Management ◽  
Best Practice ◽  
Management Practices ◽  
Social Science Research ◽  
Science Research ◽  
Current Data ◽  
Research Centre ◽  
Data Collection And Analysis ◽  
Management Plans

Data is the new buzzword in academic libraries, as policy increasingly mandates that data must be open and accessible, funders require formal data management plans, and institutions are implementing guidelines around best practice. Given concerns about the current data management practices of researchers, this paper reports on the initial findings from a project being undertaken at Griffith University to apply a conceptual (A-COM-B) framework to understanding researchers’ behaviour. The objective of the project is to encourage the use of institutionally endorsed solutions for research data management. Based on interviews conducted by a team of librarians in a small, social science research centre, preliminary results indicate that attitude is the key element which will need to be addressed in designing intervention strategies to modify behaviour. The paper concludes with a discussion of the next stages in the project, which involve further data collection and analysis, the implementation of targeted strategies, and a follow-up activity to assess the extent of modifications to current undesirable practices.

scholarly journals On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology

2019 ◽  
Vol 29 (1) ◽  
pp. 1 ◽  
Author(s):  
Heather Andrews Mancilla ◽  
Marta Teperek ◽  
Jasper Van Dijck ◽  
Kees Den Heijer ◽  
Robbert Eggermont ◽  
...  
Keyword(s):  
Data Management ◽  
Cultural Change ◽  
Management Practices ◽  
Research Data ◽  
Research Data Management ◽  
University Of Technology

scholarly journals On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology

2019 ◽  
Author(s):  
Heather Andrews ◽  
Marta Teperek ◽  
Jasper van Dijck ◽  
Kees den Heijer ◽  
Robbert Eggermont ◽  
...  
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Management Training ◽  
Research Data ◽  
Data Repositories ◽  
Automatic Data ◽  
Awareness Campaigns ◽  
Research Data Management ◽  
University Of Technology ◽  
Data Stewardship

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties. The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018. The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty. Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories. The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results. Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

scholarly journals Data management and sharing: Practices and perceptions of psychology researchers

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252047
Author(s):  
John A. Borghi ◽  
Ana E. Van Gulick
Keyword(s):  
Data Management ◽  
Management Practices ◽  
New Technologies ◽  
Research Process ◽  
Data Organization ◽  
Scholarly Output ◽  
Data Practices ◽  
Data Collection And Analysis ◽  
Human Participants

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

scholarly journals Data Management and Sharing: Practices and Perceptions of Psychology Researchers

2020 ◽  
Author(s):  
John Borghi ◽  
Ana Van Gulick
Keyword(s):  
Data Management ◽  
Management Practices ◽  
New Technologies ◽  
Research Process ◽  
Data Organization ◽  
Scholarly Output ◽  
Data Practices ◽  
Data Collection And Analysis ◽  
Human Participants

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

scholarly journals Practices and Infrastructures for ML Systems – An Interview Study

2021 ◽  
Author(s):  
Dennis Muiruri ◽  
Lucy Ellen Lwakatare ◽  
Jukka K. Nurminen ◽  
Tommi Mikkonen
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Interview Study ◽  
The State ◽  
Data Driven ◽  
Software Systems ◽  
State Of Practice ◽  
Model Training

<div> <div> <div> <p>The best practices and infrastructures for developing and maintaining machine learning (ML) enabled software systems are often reported by large and experienced data-driven organizations. However, little is known about the state of practice across other organizations. Using interviews, we investigated practices and tool-chains for ML-enabled systems from 16 organizations in various domains. Our study makes three broad observations related to data management practices, monitoring practices and automation practices in ML model training, and serving workflows. These have limited number of generic practices and tools applicable across organizations in different domains. </p> </div> </div> </div>

Data Management in an Epidemiological Study : Experiences from the Iowa 500 Field Follow-up and Family Study

1980 ◽  
Vol 19 (01) ◽  
pp. 37-41
Author(s):  
R. F. Woolson ◽  
M. T. Tsuang ◽  
L. R. Urban
Keyword(s):  
Data Management ◽  
Epidemiological Study ◽  
Family Study ◽  
Data Sets ◽  
Data Handling ◽  
Management Decisions ◽  
Numerous Data ◽  
Made In

We are now conducting a forty-year follow-up and family study of 200 schizophrenics, 325 manic-depressives and 160 surgical controls. This study began in 1973 and has continued to the present date. Numerous data handling and data management decisions were made in the course of collecting the data for the project. In this report some of the practical difficulties in the data handling and computer management of such large and bulky data sets are enumerated.

Sign in / Sign up

Export Citation Format

Share Document