Monthly Archives: July 2021

How to Build a Monster Model of Well-Being: Part 5

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This is Part 5 of the blog series on the monster model of well-being. The first parts developed a model of well-being that related life-satisfaction judgments to affect and domain satisfaction. I then added the Big Five personality traits to the model (Part 4). The model confirmed/replicated the key finding that neuroticism has the strongest relationship with life-satisfaction, b ~ .3. It also showed notable relationships with extraversion, agreeableness, and conscientiousness. The relationship with openness was practically zero. The key novel contribution of the monster model is to trace the effects of the Big Five personality traits on well-being. The results showed that neuroticism, extraversion, and agreeableness had broad effects on various life domains (top-down effects) that mediated the effect on global life-satisfaction (bottom-up effect). In contrast, conscientiousness was only instrumental for a few life domains.

The main goal of Part 5 is to examine the influence of personality traits at the level of personality facets. Various models of personality assume a hierarchy of traits. While there is considerable disagreement about the number of levels and the number of traits on each level, most models share a basic level of traits that correspond to traits in the everyday language (talkative, helpful, reliable, creative) and a higher-order level that represents covariations among basic traits. In the Five factor model, the Big Five traits are five independent higher-order traits. Costa and McCrae’s influential model of the Big Five recognizes six basic-level traits called facets for each of the Big Five traits. Relatively few studies have conducted a comprehensive examination of personality and well-being at the facet level (Schimmack, Oishi, Furr, & Funder, 2004). A key finding was that the depressiveness facet of neuroticism was the only facet with unique variance in the prediction of life-satisfaction. Similarly, the cheerfulness facet of extraversion was the only extraversion facet that predicted unique variance in life-satisfaction. Thus, the Mississauga family study included measures of these two facets in addition to the Big Five items.

In Part 5, I add these two facets to the monster model of well-being. Consistent with Big Five theory, I allowed for causal effects of Extraversion on Cheerfulness and from Neuroticism to Depressiveness. Strict hierarchical models would assume that each facet is related to only one broad factor. However, in reality basic-level traits can be related to multiple higher-order factors, but not much attention has been paid to secondary loadings of the depressiveness and cheerfulness facets on the other Big Five factors. In one study that controlled for evaluative bias, I found that depressiveness had a negative loading on conscientiousness (Schimmack, 2019). This relationship was confirmed in this dataset. However, additional relations improved model fit. Namely, cheerfulness was related to lower neuroticism and higher agreeableness and depressiveness was related to lower extraversion and agreeableness. Some of these relations were weak and might be spurious due to the use of short three-item scales to measure the Big Five.

The monster model combines two previous mediation models that link the Big Five personality traits to well-being. Schimmack, Diener, and Oishi (2002) proposed that affective experiences mediate the effects of extraversion and neuroticism. Schimmack, Oishi, Furr, and Funder (2004) suggested that the Depressiveness and Cheerfulness facets mediate the effects of Extraversion and Neuroticism. The monster model proposes that extraversion’s effect is mediated by trait cheerfulness which influences positive experiences, whereas neuroticism’s effect is mediated by trait depressiveness which in turn influences experiences of sadness.

When this model was fitted to the data, depressiveness and cheerfulness fully mediated the effect of extraversion and neuroticism. However, extraversion became a negative predictor of well-being. While it is possible that the unique aspects of extraversion that are not shared with cheerfulness have a negative effect on well-being, there is little evidence for such a negative relationship in the literature. Another possible explanation for this finding is that cheerfulness and positive affect (happy) share some method variance that inflates the correlation between these two factors. As a result, the indirect effect of extraversion is overestimated. When this shared method variance is fixed to zero and extraversion is allowed to have a direct effect, SEM will use the free parameter to compensate for the overestimation of the indirect path. The ability to model shared method variance is one of the advantages of SEM over mediation tests that rely on manifest variables and assume perfect measurement of constructs. Figure 1 shows the correlation between measures of trait PA (cheerfulness) and experienced PA (happy) as a curved arrow. A similar shared method effect was allowed for depressiveness and experienced sadness (sad), although it turned out be not significant.

Exploratory analysis showed that cheerfulness and depressiveness did not fully mediate all effects on well-being. Extraversion, agreeableness, and conscientiousness had additional direct relationships on some life-domains that contribute to well-being. The final model remained good overall fit and modification indices did not show notable additional relationships for the added constructs, chi2(1387) = 1914, CFI = .980, RMSEA = .017.

The standardized model indirect effects were used to quantify the effect of the facets on well-being and to quantify indirect and direct effects of the Big Five on well-being. The total effect of Depressiveness was b = -.47, Z = 8.8. About one-third of this effect was directly mediated by sadness, b = -.19. Follow-up research needs to examine how much of this relationship might be explained by risk factors for mood disorders as compared to normal levels of depressive moods. Valuable new insights can emerge from integrating the extensive literature on depression and life-satisfaction. The remaining effects were mediated by top-down effects of depressiveness on domain satisfactions (Payne & Schimmack, 2020). The present results show that it is important to control for these top-down effects in studies that examine the bottom-up effects of life domains on life-satisfaction.

The total effect of cheerfulness was as large as the effect of depressiveness, b = .44, Z = 6.6. Contrary to depressiveness, the indirect effect through happiness was weak, b = .02, Z = 0.6 because happy did not make a significant unique contribution to life-satisfaction. Thus, all of the effects were mediated by domain satisfaction.

In sum, the results for depressiveness and cheerfulness are consistent with integrated bottom-up-top-down models that postulate top-down effects of affective dispositions on domain satisfaction and bottom-up effects from domain satisfaction to life-satisfaction. The results are only partially consistent with models that assume affective experiences mediate the effect (Schimmack, Diener, & Oishi, 2002).

The effect of neuroticism on well-being, b = -.36, Z = 10.7, was fully mediated by depressiveness, b = -.28 and cheerfulness, b = -.08. Causality is implied by the assumption that neuroticism is a common cause of specific dispositions for anger, anxiety, depressiveness and other negative affects that is made in hierarchical models of personality traits. If this assumption were false, neuroticism would only be a correlate of well-being and it would be even more critical to focus on depressiveness as the more important personality trait related to well-being. Thus, future research on personality and well-being needs to pay more attention to the depressiveness facet of neuroticism. Too many short neuroticism measures focus exclusively or predominantly on anxiety.

Following Costa and McCrae (1980), extraversion has often been considered a second important personality trait that influences well-being. However, quantitatively the effect of extraversion on well-being is relatively small, especially in studies that control for shared method variance. The effect size for this sample was b = .12, a statistically small effect, and a much smaller effect than for its cheerfulness facets. The weak effect was a combination of a moderate positive effect mediated by cheerfulness, b = .32, and a negative effect that was mediated by direct effects of extraversion on domain satisfactions, b = -.23. These results show how important it is to examine the relationship between extraversion and well-being at the facet level. Whereas cheerfulness explains why extraversion has positive effects on well-being, the relationship of other facets with well-being require further investigation. The present results make it clear that a simple reason for positive relationships between extraversion and well-being is the cheerfulness facet. The finding that individuals with a cheerful disposition evaluate their lives more positively may not be surprising or may even appear to be trivial, but it would be a mistake to omit cheerfulness from a causal theory of well-being. Future research needs to uncover the determinants of individual differences in cheerfulness.

Agreeableness had a moderate effect on well-being, b = .21, Z = 5.8. Importantly, the positive effect of agreeableness was fully mediated by cheerfulness, b = .17 and depressiveness, b = .09, with a small negative direct effect on domain satisfactions, b = -.05, which was due to lower work satisfaction for individuals high in agreeableness. These results replicate Schimmack et al.’s (2004) findings that agreeableness was not a predictor of life-satisfaction, when cheerfulness and depressiveness were added to the model. This finding has important implications for theories of well-being that see a relationship between morality, empathy, and prosociality and well-being. The present results do not support this interpretation of the relationship between agreeableness and well-being. The results also show the importance of taking second order relationships more seriously. Hierarchical models consider agreeableness to be unrelated to cheerfulness and depressiveness, but simple hierarchical models do not fit actual data. Finally, it is important to examine the causal relationship between agreeableness and affective facets. It is possible that cheerfulness influences agreeableness rather than agreeableness influencing cheerfulness. In this case, agreeableness would be a predictor but not a cause of higher well-being. However, it is also possible that an agreeable disposition contributes to a cheerful disposition because agreeableness people may be more easily satisfied with reality. In any case, future studies of agreeableness and related traits and well-being need to take potential relationships with cheerfulness and depressiveness into account.

Conscientiousness also has a moderate effect on well-being, b = .19, Z = 5.9. A large portion of this effect is mediated by the Depressiveness facet of Neuroticism, b = .15. Although a potential link between Conscientiousness and Depressiveness is often omitted from hierarchical models of personality, neuropsychological research is consistent with the idea that conscientiousness may help to regulate negative affective experiences. Thus, this relationship deserves more attention in future research. If causality were reversed, conscientiousness would have only a trivial causal effect on well-being.

In short, adding cheerfulness and depressiveness facets to the model provided several new insights. First of all, the results replicated prior findings that these two facets are strong predictors of well-being. Second, the results showed that Big Five predictors are only weak unique predictors of well-being when their relationship with Cheerfulness and Depressiveness is taken into account. Omitting these important predictors from theories of well-being is a major problem of studies that focus on personality traits at the Big Five level. It also makes theoretical sense that cheerfulness and depressiveness are related to well-being. These traits influence the emotional evaluation of people’s lives. Thus, even when objective life circumstances are the same, a cheerful individual is likely to look at the bright side and see the their lives with rose colored glasses. In contrast, depression is likely to color live evaluations negatively. Longitudinal studies confirm that depressive symptoms, positive affect, and negative affect are influenced by stable traits (Anusic & Schimmack, 2016; Desai et al., 2012). Furthermore, twin studies show that shared genes contribute to the correlation between life-satisfaction judgments and depressive symptoms (Nes et al., 2013). Future research needs to examine the biopsychosocial factors that cause stable variation in dispositional cheerfulness and depressiveness that contribute to individual differences in well-being.

How to end the hegemony of English in scientific research | USA | EL PAÍS in English

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“last year 84% of researchers from Ibero-American countries – where Spanish or Portuguese is spoken – published their own work in English instead of their native tongues.

“Only 13% of scientists in Spain presented their work in Spanish, followed by 12% of those in Mexico, 16% in Chile, and around 20% in Argentina, Colombia and Peru,” reads the report….

German, French and Russian, which were once commonly used in various scientific publications, are now in a similar predicament: under 1% of all papers, reviews or academic conferences that appeared in scientific journals in 2020 were written in those languages….

The situation has to do not just with science, but with geopolitics, he adds. “Ibero-American countries have fallen into the trap of Anglo private industries,” said Badillo. “States pay scientists to investigate; we produce the knowledge, give it away to the big journals, thereby donating the findings of our work, and then these publications charge a truly astounding amount to the national science systems in order to access the results of our own investigations.” Ultimately, most citizens are unable to access the science that they are funding with their taxes, because it is only available in publications that charge for reading content that is written in a different language anyway….

There are three reasons for this “dictatorship of English,” as the authors of the study called it….

The third reason is tied to, and determines, the other two. “There are two major international companies, Elsevier and Clarivate Analytics, that have privatized the evaluation systems for the quality of science; they produce the international indexes listing the impact factor of journals that have been favoring English for decades,” said Badillo….

The consequences are numerous. One of them is limited access to knowledge because of the language barrier….

 

The answer proposed by the OEI and the Real Instituto Elcano is to move towards open science, a movement to make scientific research and dissemination – including publications and databases – free and accessible to all citizens. “Science needs to get out of the ivory tower where it has been bureaucratized for years, and enter into greater dialogue with society,” insisted Badillo, pointing to tools that could help with the change of paradigm. “Artificial intelligence and automatic translation should help us guarantee access to science. It would be ideal to see, in the short run, an option to read the contents of each scientific article translated not just into Spanish or Portuguese but Korean, Mandarin or any other language.” ”

Yes! We’re open. Open science and the future of academic practices in translation and interpreting studies | Olalla-Soler | Translation & Interpreting

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Abstract:  This article offers an overview of open science and open-science practices and their applications to translation and interpreting studies (TIS). Publications on open science in different disciplines were reviewed in order to define open science, identify academic publishing practices emerging from the core features of open science, and discuss the limitations of such practices in the humanities and the social sciences. The compiled information was then contextualised within TIS academic publishing practices based on bibliographic and bibliometric data. The results helped to identify what open-science practices have been adopted in TIS, what problems emerge from applying some of these practices, and in what ways such practices could be fostered in our discipline. This article aims to foster a debate on the future of TIS publishing and the role that open science will play in the discipline in the upcoming years.

 

“Introducing Reproducibility to Citation Analysis” by Samantha Teplitzky, Wynn Tranfield et al.

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Abstract:  Methods: Replicated methods of a prior citation study provide an updated transparent, reproducible citation analysis protocol that can be replicated with Jupyter Notebooks.

Results: This study replicated the prior citation study’s conclusions, and also adapted the author’s methods to analyze the citation practices of Earth Scientists at four institutions. We found that 80% of the citations could be accounted for by only 7.88% of journals, a key metric to help identify a core collection of titles in this discipline. We then demonstrated programmatically that 36% of these cited references were available as open access.

Conclusions: Jupyter Notebooks are a viable platform for disseminating replicable processes for citation analysis. A completely open methodology is emerging and we consider this a step forward. Adherence to the 80/20 rule aligned with institutional research output, but citation preferences are evident. Reproducible citation analysis methods may be used to analyze open access uptake, however, results are inconclusive. It is difficult to determine whether an article was open access at the time of citation, or became open access after an embargo.

“Engaging Researchers in Data Dialogues” by Moira Downey, Sophia Lafferty-Hess et al.

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Abstract:  A range of regulatory pressures emanating from funding agencies and scholarly journals increasingly encourage researchers to engage in formal data sharing practices. As academic libraries continue to refine their role in supporting researchers in this data sharing space, one particular challenge has been finding new ways to meaningfully engage with campus researchers. Libraries help shape norms and encourage data sharing through education and training, and there has been significant growth in the services these institutions are able to provide and the ways in which library staff are able to collaborate and communicate with researchers. Evidence also suggests that within disciplines, normative pressures and expectations around professional conduct have a significant impact on data sharing behaviors (Kim and Adler 2015; Sigit Sayogo and Pardo 2013; Zenk-Moltgen et al. 2018). Duke University Libraries’ Research Data Management program has recently centered part of its outreach strategy on leveraging peer networks and social modeling to encourage and normalize robust data sharing practices among campus researchers. The program has hosted two panel discussions on issues related to data management—specifically, data sharing and research reproducibility. This paper reflects on some lessons learned from these outreach efforts and outlines next steps.

 

“STEM Abstracting and Indexing (A&I) Tool Overlap Analysis” by Joshua Borycz, Alexander J. Carroll et al.

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Abstract:  Objectives: Compare journal coverage of abstract and indexing tools commonly used within academic science and engineering research.

Methods: Title lists of Compendex, Inspec, Reaxys, SciFinder, and Web of Science were provided by their respective publishers. These lists were imported into Excel and the overlap of the ISSN/EISSNs and journal titles was determined using the VLOOKUP command, which determines if the value in one cell can be found in a column of other cells.

Results: There is substantial overlap between the Web of Science’s Science Citation Index Expanded and the Emerging Sources Citation Index, the largest database with 17,014 titles, and Compendex (63.6%), Inspec (71.0%), Reaxys (67.0%), and SciFinder (75.8%). SciFinder also overlaps heavily with Reaxys (75.9%). Web of Science and Compendex combined contain 77.6% of the titles within Inspec.

Conclusion: Flat or decreasing library budgets combined with increasing journal prices result in an unsustainable system that will require a calculated allocation of resources at many institutions. The overlap of commonly indexed journals among abstracting and indexing tools could serve as one way to determine how these resources should be allocated.

“Optional Data Curation Feature Use by Harvard Dataverse Repository Users” by Ceilyn Boyd

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Abstract:  Objective: Investigate how different groups of depositors vary in their use of optional data curation features that provide support for FAIR research data in the Harvard Dataverse repository.

Methods: A numerical score based upon the presence or absence of characteristics associated with the use of optional features was assigned to each of the 29,295 datasets deposited in Harvard Dataverse between 2007 and 2019. Statistical analyses were performed to investigate patterns of optional feature use amongst different groups of depositors and their relationship to other dataset characteristics.

Results: Members of groups make greater use of Harvard Dataverse’s optional features than individual researchers. Datasets that undergo a data curation review before submission to Harvard Dataverse, are associated with a publication, or contain restricted files also make greater use of optional features.

Conclusions: Individual researchers might benefit from increased outreach and improved documentation about the benefits and use of optional features to improve their datasets’ level of curation beyond the FAIR-informed support that the Harvard Dataverse repository provides by default. Platform designers, developers, and managers may also use the numerical scoring approach to explore how different user groups use optional application features.

Home | COAR Asia OA Meeting 2021: Innovation, Growth and Sustainability of Open Scholarship in Asia (Oct 25-27, 2021) | SMU Libraries

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The virtual 6th meeting of COAR Asia OA will be held 25-27 October 2021. The meeting will discuss the latest trends in open access and open scholarship, with community updates from Asia. Topics include open access infrastructure, open educational resources, open peer review, research data repositories, and tools built on open data. The meeting will be a venue for information exchange between Asian communities.

Programme: https://library.smu.edu.sg/asiaoa2021#programme

Job: Senior Manager: Digital Equity (Deadline: August 15, 2021) | Wellcome

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Technology is not neutral. Who builds it, what it does, how it’s made and how people using and affected by it are involved are central to making sure the benefits of digital technology in science and health are experienced equitably.

The Senior Manager: Digital Equity is responsible for the Data for Science and Health (DSH) team’s work to improve the inclusivity and reach of data science and digital tools. They’ll do this by leading a team to understand, engage with and support communities relevant to data science that advances the strategies of Wellcome’s Health Challenges of Infectious Disease, Mental Health and Climate Change and the broad base of Discovery Research. 

They will be part of the DSH senior management team and act as the main interface with other managers in Wellcome’s Research Programmes department on matters related to digital equity.  

Main Responsibilities:

Lead and manage a team of multi-disciplinary experts to set, support and advance a common agenda for advancing digital equity in science and health by understanding, engaging with and supporting relevant communities  These communities include: data subjects: patients and people about whom data are captured; data collectors: healthcare professionals and scientists involved in capturing data and data users: researchers; research software engineers and data scientists.  

Manage the interface between the Digital Equity team and other teams in Research Programmes: generously sharing expertise and developing productive relationships, a mutual understanding of priorities and shared objectives aligned with the strategies of the Health Challenges and Discovery Research. 

Oversee delivery of the Understanding Patient Data Program and Wellcome’s Data Prizes.

Oversee and prioritise allocation of the team’s time and budget to advance the shared objectives of the Research Programmes team. 

Draw on appropriate expertise and data from across physical, biological, social sciences, humanities, industry and other funders to develop a strategic view of the landscape of communities relevant to digital equity issues in Wellcome’s strategy, and act on opportunities.   

Oversee the funding of projects and programs through effective design and execution of mechanisms in including RfPs, contracts, competitions, funding calls and discretionary awards, with hands-on work shaping and reviewing material as needed. 

Convene a data science community of practice in Wellcome to build capacity and support professional development around the effective funding and use of data science to advance Wellcome’s mission and create mutual visibility around and common approaches to key areas of work on digital technology and policy. 

Advocate for Wellcome and the role of trustworthy data science in achieving the organisation’s strategy at national and international levels, identifying and pursuing opportunities for the DSH team to provide leadership and influence decision making.  

Build, manage, lead and motivate an integrated, inclusive and flexible team, ensuring that people with the right range of skills and experience are recruited and retained, and that their skills and professional capabilities are maximised. 

Play an active leadership role in the Data for Science and Health senior management team. 

Set and monitor quarterly Objectives and Key Results for the DSH team  

Be the owner of the risk and control environment for your area and be accountable for the quality of you and your team’s outputs 

Skills and Experience:

Good knowledge of the current state of and trends in digital technologies in science and health research and practice internationally.

Demonstrable understanding of issues relating to uses of scientific and health-relevant data and building trustworthy digital systems around them.  

Experience of designing and interpreting public attitudes and engagement work. 

Experience building, leading and managing teams with a culture of mutual trust, psychological safety and effective delivery against clear objectives. 

Experience designing and delivering complex programs of work in large organisations 

Experience / knowledge of working with and running projects with universities, funders and other relevant scientific and research organisations and communities?? 

If you are interested in the role, please make sure when submitting your application, you attach your CV and complete the questionnaire form.

Salary: circa £84,000.00

Contract Type:  Permanent

Advert closing date: 15th August

Job: Technology Lead (Deadline: Aug 01, 2021) | Wellcome

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The Data for Science and Health (DSH) team is looking to recruit two Technology Leads. This team will develop, shape and execute projects related to digital technology which advance the strategies of Wellcome’s Health Challenges and Discovery Research. 

The Technology Lead will be responsible for:

Taking an entrepreneurial approach to developing, evaluating and executing projects related to digital tools which advance and cut across the objectives of Wellcome’s Health Challenges (HC) and Discovery Research (DR) strategies. 

Drawing on appropriate expertise and data from across physical, biological and social sciences, humanities, industry and other funders to develop a strategic view of the digital technology landscape relevant to Wellcome,   

Working with the Senior Manager, Digital Technology to develop productive relationships with other teams in Research Programmes by generously sharing expertise and developing a mutual understanding of priorities and shared objectives.  

Supporting digital tools funding across Research Programmes by helping to set and support common approaches, providing expert technical advice to other teams and working with the DSH team to develop a portfolio view of digital technologies across Wellcome’s current funding and pipeline.  

Being an effective funder of digital technology projects through efficient design and execution of landscaping projects, RfPs, contracts, funding calls and discretionary awards with hands-on work shaping and reviewing technical material as needed. 

Matrix management of one or more team members to support the delivery of DSH’s objectives 

Be the owner of the risk and control environment for your area and be accountable for the quality of you and your team’s outputs 

Contributing to the creation and development of a diverse and inclusive culture across the organisation, collaborating across departments. 

Experience & Skills:

Good knowledge of the current state of and trends in digital technologies in science and health research and practice 

Training in a quantitative, computational discipline with a high-level understanding of, the data and software systems used and being developed in science and health. 

Specific knowledge of or training in of one or more of Wellcome’s Health Challenges (climate and health, infectious disease, mental health) or training in clinical, biological, or physical science highly desirable. 

Experience of working with health relevant or scientific data in a technical capacity.  

Experience delivering complex work against agreed objectives in large organisations 

Experience / knowledge of working with and running projects with universities, funders and other relevant scientific and research organisations and communities?? 

If you are interested in the role, please make sure when submitting your application, you attach your CV and complete the questionnaire form

Salary: circa £68,000

Contract Type: Permanent

Advert closing date:  1st of August

Job: Technology Manager (Deadline: Aug 01, 2021) | Wellcome

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The Data for Science and Health team is looking to recruit two Technology Managers. This team is responsible for, shaping and executing projects related to digital technology which advance the strategies of Wellcome’s Health Challenges and Discovery Research. 

The Technology Manager will be responsible for:

Taking an entrepreneurial approach to evaluating and executing projects related to digital tools which advance and cut across the objectives of Wellcome’s Health Challenges (HC) and Discovery Research (DR) strategies. 

Drawing on appropriate expertise and data from across physical, biological and social sciences, humanities, industry and other funders to develop a strategic view of the digital technology landscape relevant to Wellcome,   

Working with the Senior Manager, Digital Technology and Technology Leads to develop productive relationships with other teams in Research Programs.

Supporting digital tools funding across Research Programs by helping to set and support common approaches, providing expert technical advice to other teams and working with the DSH team to develop a portfolio view of digital technologies across Wellcome’s current funding and pipeline.  

Be an effective funder of digital technology projects through efficient design and execution of landscaping projects, RfPs, contracts, funding calls and discretionary awards with hands-on work shaping and reviewing technical material as needed. 

Matrix management of one or more team members to aid in the delivery of DSH’s objectives 

Be the owner of the risk and control environment for your area and be accountable for the quality of you and your team’s outputs 

Contributing to the creation and development of a diverse and inclusive culture across the organisation, and departments. 

Exercising cost control and manage expenditure to work within the agreed operating budget 

Ensuring adherence to our compliance policies.?? 

Skills and Experience:  

Good knowledge of the current state of and trends in digital technologies in science and health research and practice 

Training in a quantitative, computational discipline with a high-level knowledge of, the data and software systems used and being developed in science and health. 

Specific knowledge of or training in of one or more of Wellcome’s Health Challenges (climate and health, infectious disease, mental health) or training in clinical, biological, or physical science highly desirable. 

Experience of working with health relevant or scientific data in a technical capacity.  

 If you are interested in the role, please make sure when submitting your application, you attach your CV and complete the questionnaire form

Salary: circa £59,000

Contract Type: Permanent

Advert closing date: 1st of August

Current market rates for scholarly publishing services

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Abstract:  For decades, the supra-inflation increase of subscription prices for scholarly journals has concerned scholarly institutions. After years of fruitless efforts to solve this “serials crisis”, open access has been proposed as the latest potential solution. However, also the prices for open access publishing are high and are rising well beyond inflation. What has been missing from the public discussion so far is a quantitative approach to determine the actual costs of efficiently publishing a scholarly article using state-of-the-art technologies, such that informed decisions can be made as to appropriate price levels. Here we provide a granular, step-by-step calculation of the costs associated with publishing primary research articles, from submission, through peer-review, to publication, indexing and archiving. We find that these costs range from less than US$200 per article in modern, large scale publishing platforms using post-publication peer-review, to about US$1,000 per article in prestigious journals with rejection rates exceeding 90%. The publication costs for a representative scholarly article today come to lie at around US$400. These results appear uncontroversial as they not only match previous data using different methodologies, but also conform to the costs that many publishers have openly or privately shared. We discuss the numerous additional non-publication items that make up the difference between these publication costs and final price at the more expensive, legacy publishers.

 

Roundtable of Experts on Data Citation | SSHOPENCLOUD

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“On May 20th 2021, SSHOC hosted a Roundtable of Experts for Data Citation, to stimulate discussion on data citation in the Social Sciences and Humanities (SSH). The session was led by CNRS and had around 30 participants, including invited speakers from UGOE, CLARIN, CNR/ISTI, the Turing Institute, the Observatory of Paris – PSL Research University, Vienna – RDA data citation WG, OpenAIRE and CODATA. 

This event followed the discussions that began during a joint event between SSHOC, FREYA, and EOSC-hub – “Realising the European Open Science Cloud” – in November. The session was focused on data citation, and different approaches and experiences related to data citation were discussed by speakers from SSHOC and beyond.

After an inventory of SSH citation practices, we began developing a prototype to implement what we called “FAIR SSH Data Citation’’. Based on that work, we drafted a first set of recommendations about data citation, adapted to the specific needs of SSH. For these discussions we invited experts from known organizations (e.g. RDA, OpenAire, CODATA, Turing Institute, Observatory of Paris) in order to hear their thoughts and feedback on the work of our task as well as data citation in the SSH in general….”

Speculative Annotation Invites Public to Interact with Digitized Collections at the Library of Congress | Library of Congress

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“Students, educators and learners of all ages are invited to interact with select items in the Library’s collections with the launch of Speculative Annotation, the latest experiment from LC Labs.

Created by artist and 2021 Innovator in Residence Courtney McClellan, Speculative Annotation is an open-source dynamic web application and public art project. The app presents a unique mini collection of free-to-use items from the Library for students, teachers and learners to annotate through captions, drawings and other types of mark-making. As a special feature for Speculative Annotation users, the app includes a collection of informative, engaging annotations from Library experts and resources on the Library’s website….”