Credit, time, and personality: The human challenges to sharing scholarly work using Web 2.0

Acord, Sophia Krzys; Harley, Diane

doi:10.1177/1461444812465140

Cited by 55 publications

(71 citation statements)

References 41 publications

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“…would prevent him or her from sharing data. Besides the actual sharing effort [21,23,29,33,[36][37][38][39][40][41], scholars utter concerns regarding the effort required to help others to make sense of the data [42]. The knowledge factor becomes apparent in Sieber's study [43] in which most researchers stated that data sharing is advantageous for science, but that they had not thought about it until they were asked for their opinion.…”

Section: Resources Neededmentioning

confidence: 99%

“…In the same line, Costello [34] argues that "[a]uthors may fear that their selective use of data, or possible errors in analysis, may be revealed by data publication". Many authors describe the fear of falsification as a reason to withhold data [23,27,29,34,43,101]. Few authors see a potential "commercialization of research findings" [93] as a reason not to share data (see also: [34,36,92]).…”

Section: Adverse Usementioning

confidence: 99%

“…Costello [34] encapsulates this issue: "If I release data, then I may be scooped by somebody else producing papers from them." Many other authors in our sample examine the issue of competitive misuse [5,22,23,46,48,[102][103][104]. Another issue regarding the recipient's use of data concerns a possible flawed interpretation (e.g., [21,59,89,105,106]).…”

Section: Adverse Usementioning

confidence: 99%

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What Drives Academic Data Sharing?

2014

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Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher's point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

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Section: Resources Neededmentioning

confidence: 99%

Section: Adverse Usementioning

confidence: 99%

Section: Adverse Usementioning

confidence: 99%

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What Drives Academic Data Sharing?

2014

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“…Many researches predict that Web 2.0 will enable a new world characterised by a more open and collaborative universe of ideas (Acord and Harley, 2013). SNS allow faculty to interact with other colleagues all over the world, publish their manuscript drafts, and invite other partners to comment, review and even criticise their work before publishing it in a journal (Conole, 2013).…”

Section: The First Hypothesis Claims That Communications Faculty Use mentioning

confidence: 99%

Use of Twitter among Spanish communication-area faculty: Research, teaching and visibility

Segado‐Boj

Domínguez

Rodríguez

2015

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The relationship of social media and undergraduate students has been widely analyzed but studies regarding faculty’s point of view are scarce. This study explores the use of Twitter in Spanish communication-area faculty members. A total of 211 respondents answered an online survey. Most of these answers consider Twitter a tool for enhancing public visibility rather than educational interaction. Though the youngest faculty members start considering Twitter a tool for consulting and disseminating information, communication via Twitter is still unidirectional.

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“…This means that the measures taken for each scientific community to make 340 sharing worthwhile will have to differ in their focus between them [Borgman et al, 2007; 341 Acord and Harley, 2013]. For instance, standardization of data and metadata is easier in 342 some disciplines, such as genomics, then it is in others [Acord and Harley, 2013].…”

Section: Preprintsmentioning

confidence: 99%

A research data sharing game

Pronk

Wiersma

Weerden

2014

Preprint

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7While reusing research data has evident benefits for the scientific community as a whole, 8 decisions to archive and share these data are primarily made by individual researchers. For 9 individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, 10 for example time and money. In this sense the problem of data sharing resembles a typical 11 game in interactive decision theory, more commonly known as game theory. Within this 12 framework we analyse how measures to promote sharing and reuse of research data affect 13 individuals who do and do not share data. We find that the scientific community can benefit 14 from top-down policies to enhance sharing data even when the act of sharing itself implies a 15 cost. Namely, if (almost) everyone shares, many individuals receive benefits, as datasets in 16 our model can be reused to achieve a higher efficiency (i.e. more publications, higher quality 17 papers). Surprisingly, as sharing implies a cost, even sharing individuals themselves in a 18 community in which sharing is common can gain a higher efficiency than individuals who do 19 not share in a community in which sharing is not common. In addition to these findings, we 20 find that measures to ensure better data retrieval and quality can compensate for sharing 21 costs by further enabling reuse. Nevertheless, an individual researcher who decides not to 22 share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy 23 that will lead to maximisation of individual efficiency, we see the average scientific 24 community efficiency in our model steadily drop as more individuals decide not to share. 25 With this in mind, we conclude that the key to motivate the researcher to share data lies in 26 reducing the costs associated with sharing, or even better, turning it into a benefit. 27 28

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Credit, time, and personality: The human challenges to sharing scholarly work using Web 2.0

Cited by 55 publications

References 41 publications

What Drives Academic Data Sharing?

What Drives Academic Data Sharing?

Use of Twitter among Spanish communication-area faculty: Research, teaching and visibility

A research data sharing game

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