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Carol Tenopir

Autor/a de Communication Patterns of Engineers

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PDFR55 | Informative Abstract | Qualitative Survey Research | Primary Source | Objectives: Academic librarians in the United States and Canada were surveyed to ascertain their capacity and readiness to offer a range of research data services and how their perceptions have changed since a 2011 survey of academic librarians. We address the following questions:

RQ1: Do academic librarians feel they have the knowledge, background, and skills to provide library-based research data services (RDS)?
RQ2: How do academic librarians rate the importance of RDS in the library?
RQ3: What are the factors that contribute to or inhibit the engagement of librarians in RDS?
RQ4: Are there changes in the opinions of academic librarians on these issues since the 2011 study?

Methods: Library directors who participated in a separate study of RDS practices in ACRL libraries were asked to distribute the current survey to their librarians. 168 librarians participated in this online survey yielding 146 valid responses for analysis in SPSS. Results: Academic librarians for whom RDS is an integral part of their job feel confident in the ability to provide such services. The group of librarians for whom RDS is occasional or not a part of their responsibilities are less confident but also agree that RDS is important in academic libraries. Consultative-time RDS, which is consistent with traditional library reference services are much more likely to be part of librarians’ job responsibilities. Conclusion: Many academic librarians believe that RDS are important services in academic libraries. Not all are confident in their abilities to offer all specific services, however, so there is a need for collaborative training in RDS, particularly for those librarians where RDS responsibilities are not a full-time part of their job responsibilities |

Contents
1. Introduction pg. 3
2. Related Research pg. 3
3. Methods pg. 4
4. Limitations pg. 5
5. Results pg. 5
--Table 1 Primary Library Service Area of Respondents pg. 5
--Table 2 Primary Disciplinary Focus of Respondents pg. 6
--Figure 1 Librarian Responsibilities Regarding RDS pg. 6
--Table 3 Context of RDS pg. 7
--Self Assessment of Readiness & Skill Development pg. 7
--Table 4 Self-Assessment of Skills for RDS pg. 7
--Table 5 Professional Development for RDS pg. 8
--Librarians Perceptions of RDS at Their Library pg. 8
--Table 6 Perceptions of the Importance of RDS in the Library pg. 8
--Librarians' Perception of Data and RDS pg. 8
--Table 7 Perceptions of the Importance of Data and RDS pg. 9
--Table 8 Impact of RDS pg 9
--Sentiments on Library Involvement with RDS pg. 9
--Table 9 Sentiments About Library Involvement with RDS pg. 10
--Table 10 Sometimes About the Impact of RDS on Students pg. 10
--RDS in Practice: Consultative/Informational and Technical pg 10
--Table 11 How Frequently Do You Perform the Following RDS with Faculty or Staff? pg. 11
--Table 12 How Frequently Do You Perform the Following RDS with Students pg. 12
-- Table 13 How Frequently Do You Perform the Following Research Data Service (RDS) on Data/Data Sets or Systems pg. 13
6. Motivations pg. 14
--Figure 2 If You Are Involved in RDS, What is the Single Most Important Motivation for Your Involvement? pg. 14
--Figure 3 If You Are Involved in RDS, What are the Other Motivations for Your Involvement? pg. 14
--Figure 4 If You Are Not Currently Involved in RDS, What Would Most Motivate You to Do So? pg. 15
7. Conclusion pg. 15
8. Acknowledgments pg. 15
9. References pg. 15

SA - Professional Readiness
RT - ALA Ethics Access, The Public Good, Preservation, Professionalism, Service, Social Responsibility,
BT - Librarian Competence
NT - RDS Librarian Perceptions
UF - Library Services Needs
SN - This is a qualitative survey research study to gain insight into perceptions regarding RDS. (This entry does not reference a hierarchical list)
… (més)
 
Marcat
5653735991n | Jan 14, 2024 |
PDFR54 | Informative Abstract | Qualitative Survey Research | Background: Scientific research in the 21st century is more data-intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation, and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for the verification of results and extending research from prior results. Methodology/Principal Findings: A total of 1329 scientists participated in this survey exploring current data-sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically
available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short and long term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance: Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process and the researchers themselves. New mandates for data management plans from NSF and other federal agencies and worldwide attention to the need to share and preserve data could lead to changes. Large-scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will bring attention and resources to the issue and make it easier for scientists to apply sound data management principles | A majority of respondents to this international survey of data practices are willing to share at least some of their data and re-use others’ data pending certain conditions or restrictions on use. Getting credit through formal citation, obtaining copies of articles that use the data, and learning of products or publications that use the data are just some of the conditions that will help encourage ata sharing. Initiatives such as the DataNet partners in the United States and similar projects in other parts of the world can help build the infrastructure, policies, and best practices that will encourage data sharing. Providing a secure but flexible cyberinfrastructure while promulgating best practices such as data citation and metadata use, will help to build confidence in data sharing. Although there is currently some satisfaction with tools for data collection and analysis, there is less awareness and satisfaction with tools for metadata creation and preservation. Most scientists do not believe their organization is doing a sufficient job in helping them achieve long-term data preservation and many researchers are not currently using international metadata standards. | DataONE and similar efforts should pay close attention to organizational policies and resources |

Contents
1. Introduction pg. 1
--Data Sharing pg. 2
--Data Sharing/Withholding Practices pg. 2
--Figure 1 Joint Information Systems Committee (JISC), Stages of the research and data lifecycle.pg. 2
--Table 1 Primary Work Sector pg. 3
--Individual Choice vs. Institutional Policies pg. 3
--Table 3 Data Access
--Table 2 Subject Discipline pg. 3
--Table 4 Data Types pg. 3
--Table 5 Data Issues pg. 4
--Data Sharing Tools pg. 4
--Supporting Cyberinfrastructure pg. 4 PARSE Survey, DOI
--Table 6 Data Tools pg. 4 Systems Biology Markup Language, Systems Biology Ontology, Dyrad Project,
2. Methods pg. 4
--Methodology pg. 4 Online Surveys
--Table 6 Data Tools pg. 4
--Table 7 Organizational Involvement in Data Issues pg. 5
--Table 8 Data Reuse pg. 5
--Research Instrument pg. 5
--Table 9 Metadata Standards pg. 6
3. Results and Discussion pg. 6
--Demographics of Respondents pg. 6
--Current Data Practices pg. 6
--Table 10 Data Sharing Practices pg. 6
--Table 11 Data Sharing cont..pg.7
--Data Use pg.7
--Data Practices pg. 7
--Data Management Support and Policies pg. 7
--Data Resuse pg. 7
--Table 12 Reasons for Not Making Data Electronically Available pg. 7
--Table 13 Conditions for Data Sharing pg. 8
--Table 14 Others Using Data & Using Others/ Data pg. 8
--Table 15 Conditions for Data Sharing by Subject Discipline pg. 9
--Data Sharing pg. 9
--Table 16 Data Sharing by Subject Discipline pg. 9
--Table 17 Satisfaction for Data Management by Subject Discipline pg. 10
--Table 18 Data Reuse by Subject Discipline pg. 10
--Table 19 Conditions for Data Sharing by Subject Discipline pg. 11
--Data Use by Subject Discipline pg. 11
--Table 20 Conditions for Data Sharing for Reuse by Subject Discipline pg. 11
--Table 21 Using Others' Data by Subject Discipline pg. 12
--Data Practices by Subject Discipline pg. 12
--Table 22 Using Others' Data by Subject Discipline pg. 12
--Table 23 Organizational Involvement in Data Issues by Age Group pg. 13
--Data Management Support and Policies by Subject Discipline pg. 13
--Data Reuse by Subject Discipline pg. 13
--Table 24 Data Reuse by Age Group pg. 13
--Data Sharing by Subject Discipline pg. 14
--Discipline Summary pg. 14
--Table 26 Others Using Data by Age Group pg.14
--Age: Data Management Support Policies pg. 14
--Table 27 Using Others' Data by Age Group pg. 15
--Table 28 Conditions for Dat Shring by Activity pg. 15
--Table 29 Data Access by Activity pg. 26
--Data Management Support and Policies by Activity pg. 16
--Activity Summary pg. 16
--Geographic Location pg. 16 Data Practices by Geographic Location
--Table 30 Organizational Involvement by Activity pg. 16
--Table 31 Satisfaction by Geographic Location pg. 17
--Table 32 Satisfaction with Data Management by Geographical Location pg. 17
--Table 33 Organizational Involvement in Data Issues by Geographic Location pg. 18
--Table 34 Data Reuse by Geographic Location pg. 18
4. Conclusion pg. 18
--Table 35 Conditions for Data Sharing by Geographic Location pg. 19
--Table 36 Others Using Data by Geographic Location pg. 19
--Table 37 Using Others' Data by Geographic Region pg. 20
5. Supporting Information pg. 21 Appendix S1 (not included)
6. Acknowledgements pg. 21
7. References pg. 21
8. Author Contributions pg. 21 |

SA - Archival Use & Contribution
RT - Scholarship as Conversation, Information Has Value, Infomation Creation as a Process
BT - Data Use and Reuse
NT - Archival Perceptions
UF - Determining Value of Data Reuse
SN - Survey Research to determine data archiving value; a qualitative study demonstrating a need for reusable data and findable data services. (This entry does not reference a hierarchical list)
… (més)
 
Marcat
5653735991n | Jan 13, 2024 |
PDFR53 | Informative Abstract | Qualitative Survey Study | Background: With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists worldwide. Methods: The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample
of scientific researchers in a two-wave approach in 2017–2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and employment sectors. Findings: Most respondents displayed what we describe as high and mediocre-risk data practices by
storing their data on their personal computers, departmental servers, or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half were satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about half thought that it restricted their ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage and educational programs for both
awareness and to help engender good data practices are needed | The approach by the NSF-sponsored DataONE project to re-assess the scientific community’s
data sharing perceptions, practices, and related changes over the past 3 to 4 years has been illuminating,
producing a number of both statistically and practically significant findings. Overall,
favorable perceptions of and practices surrounding data sharing are increasing, albeit gradually.
This shift varies according to researchers’ ages, geographic regions, and subject disciplines,
indicating that perhaps data sharing is more normalized among some groups than others. This
points to important implications for the promotion of data sharing and reuse, as well as a roadmap
for which groups need to be targeted in these efforts.

Contents
1. Abstract pg. 1
2. Introduction pg. 2
--Data Sharing and Withholding pg. 3
--Individual Factors pg. 3
--Institutional and Policy Factors
3. Methods pg. 4
--Participants and Sampling
--Procedure
--Survey Instrument
4. Results
--Changes in Data-Related Perceptions and Practices, 2009/2010 to 2013/2014
--Data Sharing and Reuse: Perceptions
--Data Sharing and Reuse: Practices
--Satisfaction with Data Practices
--Perceptions of Organizational Support pg. 9
5. Demographic Groups in Relation to Data Reuse and Sharing: Follow-Up Survey pg. 10
--Age pg. 10
--Geographic Location pg. 12
--Subject Discipline pg. 14
6. Discussion
--Baseline to Follow-Up Changes
--Follow-Up Survey: Demographic Factors pg. 18
--Subject Discipline pg. 20
7. Conclusions and Recommendations pg. 20
8. Limitations
9. Supporting Information - See Appendix Tables
10. Acknowledgments pg. 22
11. Author Contributions pg. 22
12. References pg. 22

SA - Survey Data Statistics
RT - Opinion Survey
BT - Perception
NT - Data Sharing
UF - Determining the current level of data reuse.
SN - A qualitative research study using surveys. (This entry does not reference a hierarchical list)
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Marcat
5653735991n | Jan 12, 2024 |
PDFR52 | Informative Abstract | Qualitative Study Using Surveys | Use of Legal Citation Without Footnotes | Background: With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination
of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists worldwide. Methods: The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-wave approach in 2017–2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and employment sectors. Findings: Most respondents displayed what we describe as high and mediocre-risk data practices by storing their data on their personal computers, departmental servers, or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half were satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to
progress in science at large, yet only about half thought that it restricted their ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage and educational programs for both awareness and to help engender good data practices are needed |

Contents
1. Introduction pg. 2
2. Fig. 1 Data Life Cycle pg. 3 From https://www.dataone.org/data-life-cycle & https://doi.org/10.1371/journal.pone.0229003.g001
3. National and Institutional Factors Impacting Data Sharing pg. 4
4. Individual Factors Impacting Data Sharing pg. 5
5. Material and Methods pg. 7
--Research Instrument
6. Results
--Fig. 2 Which of the Following Best Describes Your Primary Work Sector (n=2088)
--Fig 4 Primary Place of Employment Grouped by Region (n=2016)
--Table 2 “How much of your data do you currently store or deposit in the following locations?”
--Fig. 5 How much of your data do you currently store or deposit in the following locations?
--Fig 6 I am satisfied with the..."
--Table 3 “The following statements relate to how you store and manage your data. Tell us how much you agree with the following ways to complete the sentence: I am satisfied with. . .”
--Data Management Support and Practices
--Table 4 Crosstab between “Primary sector of employment” and “My organization or project provides (training/assistance)
--Table 5 Crosstab between “Primary Subject Discipline” and “My organization or project provides . . .”
--Data Sharing and Reuse
--Table 6 Crosstab between “Primary sector of employment” and “Satisfaction with tools for metadata, provenance information, and repositories”
--Fig. 7 Cross between “primary subject discipline” and “I would be willing to share data across a broad group of
researchers (n = 1776)
--Table 7 "The following statements relate to sharing scientific data. Tell us how much you agree with each statement”
--Barriers to Data Sharing
--Metadata pg. 16
--Table 8 “The following statements relate to your views on the use of scientific research data. Tell us how much you agree with each statement”
--Table 9 “The following statements relate to your views on the reuse of scientific research data. Tell us how much
you agree with the following ways to complete this sentence: I would have increased confidence in re-using data
collected by others if. . .”
--Fig. 8 “How often do you conduct research in which some or all of the data analyzed was collected by someone besides yourself
or members of your immediate research team?” (N = 1795)
--Fig 9 “If all or part of your data are not available to others, why not (Choose all that apply)?” (n = 2184)
-Institutional Framework pg. 18
--Fig. 10 “What metadata standards do you currently use to describe your data, if any? (Choose all that apply)”
--Table 10 Crosstab between “primary funding agency requiring data management plan” and subject discipline. (n = 1966)
--Table 11 “You have expressed agreement that your organization or project has a formal process for managing or storing data during or beyond the life of the project (short-term or long-term). Which of the following are involved with this process? (Choose all that apply)”
--Limitations
7. Discussion and Conclusions pg. 20
-- Data use/data storage: Researchers store their data in a variety of places, representing good, mediocre, and bad data practices
--Table 12 Crosstab between a primary sector and "Which of the following are involved in data management/storage in your organization?”.
--Data Reuse, Sharing and Barriers pg. 21
--Institutional Framework/Data Management Support
8. Conclusions pg. 22
9. Supoorting Information pg. 24
10. Acknowledgments pg. 24
11. Author Contributions pg. 24
12 References pg. 24 |

SA - Archival Use & Contribution, Data Tail
RT - Scholarship as Conversation, Information Has Value, Infomation Cration as a Process, Usability
BT - Data Use and Reuse, Data Management Practices
NT - Archival Perceptions, Curation of Collections
UF - Determining Value of Data Reuse and To determine the needs for Scholarly Data Management.
SN - This is a qualitative study demonstrating a need for reusable data and findable data. (This entry does not reference a hierarchical list)
… (més)
 
Marcat
5653735991n | Jan 11, 2024 |

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