Research Information Network

PDFRE2 | In this context, our key aim in the case studies we report on here has been to improve understanding of information use in the life sciences, and to provide a broader and deeper base of evidence to inform discussions about how information policy and practice can most effectively be supported and improved | Our key conclusion, therefore, is that the policies and strategies of research funders and information service providers must be informed by an understanding of the exigencies and practices of different research communities. Only thus will they be effective in optimizing the use and exchange of information, and in ensuring that they are scientifically productive and cost-effective |

Contents
Executive summary 4
1. Introduction 8
2. Summary of findings 12
3. Case studies 14
-- Figure 1: Information flow map for animal genetics and animal disease genetics (case study 1)
-- Figure 2: Information flow map for transgenesis in the chick and development of the chick embryo (case study 2)
-- Figure 3: Information flow map for epidemiology of zoonotic diseases (case study 3)
-- Figure 4: Information flow map for neuroscience (case study 4)
-- Figure 5.1: Information flow map for systems biology (case study 5)– computing-based process
-- Figure 5.2: Information flow map for systems biology (case study 5)
-- Figure 6: Information flow map for regenerative medicine (case study 6) –lab-based process
-- Figure 7: Information flow map for botanical curation (case study 7)
-- 1: Animal genetics and animal diseases 14
-- 2: Transgenesis in the chick and development of the chick embryo 16
-- 3: Epidemiology of zoonotic diseases 18
4: Neuroscience 20
5: Systems Biology 22
6: Regenerative medicine 26
7: Botanical curation 28
4. Overall observations 32
-- Diagram 1: ‘Impressionistic’ taxonomy of case study research data
5. Information lifecycle 36
-- Figure 2: Knowledge Transfer Cycle
6. Implications for institutional information services 46
7. Policy challenges and recommendations 50
8. References 55

SA - https://www.librarything.com/work/32171834/book/263016838 | https://www.librarything.com/work/31528627/book/263006277 | https://www.librarything.com/work/32149444/book/262771999 | https://www.librarything.com/work/32135039/book/262594295 | https://www.librarything.com/work/32130292/book/262528448 | https://www.librarything.com/work/32118813/book/262385548 | https://www.librarything.com/work/31520626/book/255986143 | https://www.librarything.com/work/31512246/book/255872590 | https://www.librarything.com/work/31511109/book/255847480 |
RT - Information
BT - Peer Review
NT - Collaboration
UF - This is a report on patterns of information use and exchange among researchers in the life sciences, providing insights into their practices and recommendations for improving information policy and support.
SN - (This entry does not reference a hierarchical list)… (meer)

PDFRE | Report commissioned by the Research Information Network (RIN) | www.rin.ac.uk | Researchers in classics tend to be well aware of the issues to do with publishing datasets and, up to now, they have been well served by organizations such as the Arts and Humanities Data Service (AHDS) and the Centre for Computing in the Humanities at King’s College London. Classicists are encouraged to produce data management plans and to archive their datasets by key funding bodies. There is, however, an atmosphere of uncertainty as the AHDS is to lose its funding in March 2008 and will cease to exist in its current form. The Archaeology Data Service will continue to function. This means that the official channel for giving advice to those who wish to publish data will no longer be available and that the official subject repository for published datasets will cease to exist. It is possible that the advisory role performed by the AHDS will be replaced by one or more technical reviewers. This uncertainty has led to calls for subject centers of excellence to provide advice on data-related issues. As for archiving, in the absence of the AHDS, many classicists feel that their own institution should host their datasets, but it is unclear how this should be funded or what might encourage their library or computer center colleagues to take on this responsibility|

Contents

Classics 1
Overview
Data creation 1
Form and variety of data produced 1
Purposes of data generated as research output 2
Metadata 2
Adding value to data 2
Long term viability of datasets 3
Working with the created data 4
Tools and technologies for analysis 4
“Publishing” datasets 5
Ownership of data and constraints on publication and use 6
Response to requests for datasets 6
Discovery, access and use of third party datasets 6
Discovering relevant datasets 6
Access to third party datasets 6
Use of third party datasets 7
Quality assurance 7
Quality assurance in the data creation process 7
Data management planning 7
Quality assessment of datasets 8
What motivates researchers to publish data? 8
Social and public health sciences 9
Overview 9
Data creation 10
Form and variety of data produced 10
Metadata 10
Adding value to data 11
Long term viability of datasets 11
Working with the created data 11
Tools and technologies for analysis 11
“Publishing” datasets 12
Ownership of data and constraints on publication and use 12
Response to requests for datasets 12
Discovery, access and use of third party datasets 13
Discovering relevant datasets 13
Access to third party datasets 13
Use of third party datasets 14
Quality assurance 14
Quality assurance in the data creation process 14
Data management planning 14
Quality assessment of datasets 14
What motivates researchers to publish data? 15
Push factors: the effect of policy 15
Pull factors: intrinsic rewards 16

6. Astronomy 17
Overview
Data creation 18
Form and variety of data produced 18
Metadata 19
Adding value to data 19
Long term viability of datasets 19
Working with the created data 21
Tools and technologies for analysis 21
“Publishing” datasets 21
Ownership of data and constraints on publication and use 22
Response to requests for datasets 22
Discovery, access and use of third party datasets 22
Discovering relevant datasets 22
Access to third party datasets 22
Use of third party datasets 23
Quality assurance 23
Quality assurance in the data creation process 23
Data management planning 23
Quality assessment of datasets 24
What motivates researchers to publish data? 24
Push factors: the effect of policy 24
Pull factors: intrinsic rewards 24

7. Chemical crystallography 26
Overview 26
Data creation 28
Form and variety of data produced 28
Metadata 28
Adding value to data 28
Long term viability of datasets 29
Working with the created data 29
Tools and technologies for analysis 29
“Publishing” datasets 29
Ownership of data and constraints on publication and use 30
Response to requests for datasets 31
Discovery, access and use of third party datasets 31
Discovering relevant datasets 31
Access to third party datasets 31
Use of third party datasets 31
Quality assurance 32
Quality assurance in the data creation process 32
Data management planning 32
Quality assessment of datasets 32
What motivates researchers to publish data? 32
Push factors: the effect of policy 32
Pull factors: intrinsic rewards 33

8. Genomics 34
Overview 34
Data creation 35
Form and variety of data produced 35
Metadata 37
Adding value to data 38
Long term viability of datasets 39
Working with the created data 40
Tools and technologies for analysis 40
“Publishing” datasets 40
Ownership of data and constraints on publication and use 41
Response to requests for datasets 41
Discovery, access and use of third party datasets 41
Discovering relevant datasets 41
Access to third party datasets 42
Use of third party datasets 42
Quality assurance 42
Quality assurance in the data creation process 42
Data management planning 42
Quality assessment of datasets 43
What motivates researchers to publish data? 43
Push factors: the effect of policy 43
Pull factors: intrinsic rewards 43

9. Systems biology 44
Overview 44
Data creation 45
Form and variety of data produced 45
Metadata 46
Adding value to data 46
Long term viability of datasets 46
Working with the created data 47
Tools and technologies for analysis 47
“Publishing” datasets 48
Ownership of data and constraints on publication and use 49
Response to requests for datasets 49
Discovery, access and use of third party datasets 49
Discovering relevant Datasets 49
Access to third party datasets 50
Use of third party datasets 51
Quality assurance 51
Quality assurance in the data creation Process 51
Data management planning 52
Quality assessment of datasets 52
What motivates researchers to publish data? 52
Push factors: the effect of policy 52
Pull factors: intrinsic rewards 52

10. Rural Economy and Land Use 53
Overview 53
Data creation 54
Form and variety of data produced 54
Metadata 55
Adding value to data 55
Long term viability of datasets 55
Working with the created data 55
Tools and technologies for analysis 55
“Publishing” datasets 56
Ownership of data and constraints on publication and use 56
Response to requests for datasets 56
Discovery, access and use of third-party datasets 57
Discovering relevant Datasets 57
Access to third-party datasets 57
Use of third-party datasets 58
Quality assurance 58
Quality assurance in the data creation process 58
Data management planning 58
Quality assessment of datasets 59
What motivates researchers to publish data? 59
Push factors: the effect of Policy 59
Pull factors: intrinsic rewards 61

11. Climate Science 62
Overview 62
Data creation 63
Form and variety of data produced 63
Metadata 63
Adding value to data 64
Long-term viability of datasets 64
Working with the created data 64
Tools and technologies for analysis 64
“Publishing” datasets 65
Ownership of data and constraints on publication and use 65
Response to requests for datasets 65
Discovery, access and use of third-party datasets 66
Discovering relevant Datasets 66
Access to third-party datasets 66
Use of third-party datasets 67
Quality assurance 67
Quality assurance in the data creation process 67
Data management planning 67
Quality assessment of datasets 68
What motivates researchers to publish data? 68
Push factors: the effect of Policy 69
Pull factors: intrinsic rewards 69

SA - https://www.librarything.com/work/31528627/book/263006277 | https://www.librarything.com/work/32149444/book/262771999 | https://www.librarything.com/work/32135039/book/262594295 | https://www.librarything.com/work/32130292/book/262528448 | https://www.librarything.com/work/32118813/book/262385548 | https://www.librarything.com/work/31520626/book/255986143 | https://www.librarything.com/work/31512246/book/255872590 | https://www.librarything.com/work/31511109/book/255847480 |
RT - FAIR
BT - Discovery
NT - Peer Review
UF -Preservation and accessibility: Sharing datasets ensures the preservation of valuable historical and cultural information for future generations and allows researchers and scholars to access and study primary sources, texts, artifacts, and other materials related to classical studies. ​Sharing datasets encourages collaboration among researchers from different disciplines, enabling them to combine their expertise and analyze data collectively, leading to new insights and discoveries.
SN - The document is a report commissioned by the Research Information Network (RIN) on the publication and quality assurance of research data outputs in various research areas. It is a PDF downloaded from the RIN website. (This entry does not reference a hierarchical list)… (meer)