Presentation by Cathelijn Waaijer at the 2014 Science and Technology Indicators conference in Leiden, on the influence of career prospects on the job choice of PhDs.
Presentation done to JOOUST staff to highlight challenges facing young researchers on writing grants winning proposals at the commencement of their carriers.
Any question that we want answered and any assumption or assertion that we want to challenge or investigate can become a research problem or a research topic for our study
Presentation done to JOOUST staff to highlight challenges facing young researchers on writing grants winning proposals at the commencement of their carriers.
Any question that we want answered and any assumption or assertion that we want to challenge or investigate can become a research problem or a research topic for our study
What's hot in entrepreneurship research 2013 is an international expert survey aimed at the identification of relevant topics and methods. According to the survey, the entrepreneurial process, social entrepreneurship as well as entrepreneurial thinking and behavior are currently the most relevant topics within the field of entrepreneurship research. Methods are still dominated by quantitative approaches, respondents perceive them, however, as being associated with less potential to generate new insights when compared to other analytical approaches. In particular qualitative comparative analysis (QCA) is identified as a new or underappreciated method going along with the potential for new and interesting findings.
Tutorial for beginning graduate students. Some guidelines for composing the research proposal for an MS project. Also presents the perspective of advisor and committee.
Research problem is a question that researcher wants to answer or a problem that a researcher wants to solve Identification & formulation of a research problem is the first step of the research process.
This note describes our analysis of 35 papers from CHI 2011 that aim to improve or support interaction design practice. In our analysis, we characterize how these CHI authors conceptualize design practice and the types of contributions they propose. This work is motivated by the recognition that design methods proposed by HCI researchers often do not fit the needs and constraints of professional design practice. As a complement to the analysis of the CHI papers we also interviewed 13 practitioners about their attitudes towards learning new methods and approaches. We conclude the note by offering some critical reflections about how HCI research can better support actual design practice.
What's hot in entrepreneurship research 2013 is an international expert survey aimed at the identification of relevant topics and methods. According to the survey, the entrepreneurial process, social entrepreneurship as well as entrepreneurial thinking and behavior are currently the most relevant topics within the field of entrepreneurship research. Methods are still dominated by quantitative approaches, respondents perceive them, however, as being associated with less potential to generate new insights when compared to other analytical approaches. In particular qualitative comparative analysis (QCA) is identified as a new or underappreciated method going along with the potential for new and interesting findings.
Tutorial for beginning graduate students. Some guidelines for composing the research proposal for an MS project. Also presents the perspective of advisor and committee.
Research problem is a question that researcher wants to answer or a problem that a researcher wants to solve Identification & formulation of a research problem is the first step of the research process.
This note describes our analysis of 35 papers from CHI 2011 that aim to improve or support interaction design practice. In our analysis, we characterize how these CHI authors conceptualize design practice and the types of contributions they propose. This work is motivated by the recognition that design methods proposed by HCI researchers often do not fit the needs and constraints of professional design practice. As a complement to the analysis of the CHI papers we also interviewed 13 practitioners about their attitudes towards learning new methods and approaches. We conclude the note by offering some critical reflections about how HCI research can better support actual design practice.
Three wonderful researchers gathered together a century of work on which hiring practices are related to performance in the job. Problem is, they wrote a 75 page paper about it, and that's a barrier. I've summarized their paper into less than 30 slides so you can make the case for science-based hiring in your company.
This presentation talks about need for research, the way impact of research is measured and the current trends in making research more visible. A case of econometric is dealt with,
Intuition's Fall from Grace - Algorithms and Data in (Pre)-Selection by Colin...Textkernel
On 2 June during Textkernel's conference Intelligent Machines and the Future of Recruitment, Colin Lee presented his work on the automated preselection of applicants. For this research he used data from Connexys from 441,768 applicants at 48 companies, in combination with Textkernel parsing and normalization, to develop an algorithm that predicts which applicants get invited to a job interview. Colin explains the logic behind his approach and discusses potential future applications.
We live in an age of research measurement. In this session we consider the current form of the REF, how it effects both a university’s relationship with research and the developing careers of early-career researchers. The session will also consider what you can do to make sure you are best equipped and ‘in the know’ for the demands of the REF once you apply for and start an academic job.
A Study on Job Satisfaction of Private School Teachers with Reference to Mann...ijtsrd
The project has been undertaken a study on job satisfaction of teachers working in private school with reference to Mannargudi Thiruvarur DT . Job satisfaction refers to the general attitude of employees towards their present job. Job satisfaction probably is the most widely studied variable.Its mainly involved in two variables positive and negative. The person not satisfied his her work it creates negative attitudes if satisfied it create positive attitudes. So job satisfaction is the most important factor the person involvement to do his or her work. In this research take a teacher were working in different private schools in Mannargudi analysing satisfaction level of his or her work. Monika G | Priyanka R "A Study on Job Satisfaction of Private School Teachers with Reference to Mannargudi" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30645.pdf Paper Url :https://www.ijtsrd.com/management/other/30645/a-study-on-job-satisfaction-of-private-school-teachers-with-reference-to-mannargudi/monika-g
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
140904 presentation cathelijn waaijer sti 2014 slideshare
1. Career perspectives and job choice: a
survey of recent PhD graduates of five
Dutch universities
Cathelijn J. F. Waaijer
STI conference, 4 Sept 2014
2. Numbers of PhD graduates
Source: VSNU
Disclaimer: preliminary results. Further analysis may change conclusions.
3. Sectors of employment
Auriol, Misu & Freeman (2013)
Disclaimer: preliminary results. Further analysis may change conclusions.
4. Career aspirations
Sauermann & Roach (2012)
Disclaimer: preliminary results. Further analysis may change conclusions.
5. Career perspectives in academic R&D
and their effects – some evidence
Fox & Stephan (2001)
1: poor
2: fair
3: good
4: excellent
• Uncertain prospects and long spells on temporary contracts
decrease attractiveness of scientific career according to leading
scientists (Waaijer 2014)
• Insecurity about career affects well-being of postdocs (Höge,
Brucculeri & Iwanova 2012)
Disclaimer: preliminary results. Further analysis may change conclusions.
6. Research questions
• How do PhD graduates perceive their career
perspectives in different sectors of work?
– Academic R&D
– Non-academic R&D
– Non-R&D
• Do career perspectives influence the choice of sector of
work?
– Measured effect on sector of work
– Opinion of PhD graduates
Disclaimer: preliminary results. Further analysis may change conclusions.
7. Main variables
– Career perspectives: rated very good – good – neutral – bad – very bad
• Long-term career perspectives (in general)
• Availability of permanent positions
• Usual length of period holding temporary positions
• Quality of human resource management and career policy
– Employment sector: academic R&D, non-academic R&D, non-R&D
• Constructed from variables “involved in basic research”, “involved in applied
research” and “involved in experimental development”, and description of
employer
Disclaimer: preliminary results. Further analysis may change conclusions.
8. Survey
• Follow-up to 2008 Netherlands Survey of Doctorate Recipients among
PhD graduates (April 2008 – March 2009) of:
– Delft University of Technology (engineering and technology)
– Erasmus University Rotterdam (focused on social sciences, medicine)
– Utrecht University (all scientific fields)
– Wageningen University (agricultural sciences, natural sciences)
• New: PhD graduates from Leiden University (January 2008 – April
2012): all scientific fields except economics, and engineering and
technology
• Total: 2,430 PhD graduates (half of them from Leiden)
• Surveyed sample: 2,207 PhDs; through email or LinkedIn
• Survey open for 91 days
Disclaimer: preliminary results. Further analysis may change conclusions.
9. Descriptive statistics
• 51.5% (partial) response rate
• 43.6% progressed to the final question
– Respondents were allowed to leave questions unanswered, except if a
response was required for routing
• Females: 45%
• 96.3% had paid work at time of survey
• Scientific field of PhD by university (in %)
Delft Leiden Rotterdam Utrecht Wageningen Total
Medical and health sciences 0 38 61 36 9 34
Natural sciences 17 23 5 33 70 26
Social sciences 7 18 31 16 9 17
Humanities 4 19 3 9 1 13
Engineering and technology 73 3 1 7 11 11
Disclaimer: preliminary results. Further analysis may change conclusions.
10. Perception of career perspectives
Disclaimer: preliminary results. Further analysis may change conclusions.
11. Long-term career perspectives by sector
of work
Opinion on: Academic R&D Non-academic R&D Non-R&D
Current sector of work
Ac R&D
N-Ac R&D
Non-R&D
Ac R&D
N-Ac R&D
Non-R&D
Ac R&D
N-Ac R&D
Disclaimer: preliminary results. Further analysis may change conclusions.
Non-R&D
Very good 11 6 5 6 14 6 8 15 14
Good 31 17 10 36 50 30 34 49 47
Neutral 22 28 30 39 30 49 44 32 31
Bad 26 37 40 16 4 14 12 2 7
Very bad 10 12 16 3 2 1 3 2 1
p-value <0.001 <0.001 <0.001
12. Self-reported influence of perspectives in
academic R&D on job choice by sector
Current sector of work Ac R&D Non-ac R&D Non-R&D Total p
Long-term career
perspectives
53 57 50 54 0.443
Availability of permanent
positions
35 49 45 40 0.001
Usual length of period
holding a temporary
position
23 35 35 27 0.001
Quality of HRM/career
policy
12 25 23 17 <0.001
% who agree “strongly” or “very strongly”, in %
Disclaimer: preliminary results. Further analysis may change conclusions.
13. Other factors in job choice
Disclaimer: preliminary results. Further analysis may change conclusions.
14. Factors important for job choice – by sector of
work
Ac R&D N-ac R&D Non-R&D p-value
Intellectual challenge 87 78 68 <0.001
Degree of independence 76 58 57 <0.001
Possibility to develop new skills 69 77 62 0.008
Creativeness 66 60 39 <0.001
Job security 28 41 43 <0.001
Salary 24 45 38 <0.001
Job opportunities within
19 35 24 <0.001
organization
Benefits 21 31 17 0.002
Availability of permanent jobs within
21 28 21 0.049
organization
Personal and family-related
circumstances
25 16 17 0.006
Organization's career policy and
HRM
8 21 11 <0.001
Disclaimer: preliminary results. Further analysis may change conclusions.
15. Multinomial logistic regression – several
factors included
• Perception of career perspectives in academic R&D
• Perception of own scientific oeuvre
• Availability of sufficient job opportunities
• Years since PhD
• Field of PhD
• Which job characteristics play a role in job choice
• Personal characteristics (nationality, gender, age)
• Pseudo R2: Cox and Snell 0.369; Nagelkerke 0.449
Disclaimer: preliminary results. Further analysis may change conclusions.
16. Non-academic R&D cf. academic R&D
• Career perspectives in academic R&D:
– More positive about long-term career perspectives -> less likely to work in non-academic
R&D
– More positive about HRM -> more likely
• Other factors:
– More positive about sufficient number of positions in preferred sector of work -> more
likely
– Medical sciences, social sciences, humanities -> less likely than engineering
– Value intellectual challenge, degree of independence and personal circumstances ->
less likely
– Value contribution to society, salary and job opportunities within organization -> more
likely
– Dutch nationals -> more likely
Disclaimer: preliminary results. Further analysis may change conclusions.
17. Non-R&D cf. academic R&D
• Career perspectives in academic R&D
– More positive about availability of permanent positions -> less likely to
work in non-R&D
– More positive about HRM -> more likely
• Other factors:
– More positive about own scientific oeuvre -> less likely
– Value creativeness, intellectual challenge, and personal and family-related
circumstances -> less likely
– Value job opportunities within organization -> more likely
Disclaimer: preliminary results. Further analysis may change conclusions.
18. Conclusions
• Career perspectives perceived as much worse in academic R&D than non-academic
R&D and non-R&D
• Difference in career perspectives between sectors perceived as larger by those
working in non-academic R&D and non-R&D
• Self-reported influence of different career aspects in academic R&D quite large,
even more so for PhDs in non-academic R&D and non-R&D
• Aspects of personal development and job content main factors influencing job
choice, but less so for people outside academic R&D
• Perception of career perspectives plays a small but significant role in job choice
(controlled for other variables)
Disclaimer: preliminary results. Further analysis may change conclusions.
19. Acknowledgements
• Cornelis van Bochove
• Rosalie Belder
• Inge van der Weijden
• Rens van de Schoot
• Hans Sonneveld
• Moniek de Boer
• Danique van den
Hanenberg
• Malu Kuhlmann
• Lisa van Leeuwen
• Lisette van Leeuwen
• Suze van der Luijt-Jansen
• Laura de Ruiter
• Bert van der Wurff
20. Other factors that might play a role
Push factors
• (Own perception) of academic quality (e.g., Sanz-Menendez et al.)
• Preference for current job
• Field of PhD
• Year of PhD
Pull factors
• Job characteristics job satisfaction is acquired from (personal
development vs. terms of employment: “taste for science” cf.
Sauermann & Roach)
• Personal characteristics: gender, age, nationality
Disclaimer: preliminary results. Further analysis may change conclusions.
23. Positions for PhD graduates
Disclaimer: preliminary results. Further analysis may change conclusions.
Editor's Notes
Academic: university, academic hospital, research institute, or university of applied sciences/college
Conclusion: PhD graduates rate the long-term career perspectives of their own sector of work better than people not working in that sector. The same pattern for the availability of permanent positions. But only relatively: those working in academic R&D are still quite pessimistic about opportunities in academic R&D, just a little bit less so than people working elsewhere. With regards to the usual length of the period holding a temporary position and quality of HRM: only graduates working in non-academic R&D think these aspects are better in their own sector than other PhDs. Howev
From these figures it seems that the career perspectives in academic R&D influence PhDs’ job choice to a considerable extent, more so when they chose to work in non-academic R&D and non-R&D. However, also other factors might have played a role.
Not only the perception of career perspectives play a role in job choice, also other factors are important. One of these is which job characteristics PhD graduates value when choosing their job (cf. Sauermann & Roach).
Conclusions from this slide: PhD graduates say they are more guided by factors relating to personal development and job content than to terms of employment. Of the former, intellectual challenge, degree of independence and the possibility to develop new skills are the factors most often ticked. Of the latter, these are the degree to which a job provides opportunities for career advancement, job security and salary.
Next, we’ll investigate whether there are differences between PhDs working in different sectors as to how often they ticked the factors
Conclusion: for PhDs working in academic R&D factors relating to personal development are more important in job choice than for persons working in non-academic R&D and non-R&D. On the other hand, some terms of employment are more important for those working in non-academic R&D. Exceptions are the possibility to develop new skills, which are more important for those working in non-academic R&D, and personal and family-related circumstances, which play a role more often for those in academic R&D.
Variables (t < -2 or t>2) and p < 0.05
Findings: main factors influencing job choice are whether sufficient positions in the preferred sector were available, the field of PhD, and a “taste for science”, i.e., PhDs working in non-academic R&D value intellectual challenge and degree of independence less. However, they value their contribution to society, salary and the job opportunities within the organization more. They are less influenced by personal and family-related circumstances than those working in academic R&D, and more often have the Dutch nationality. However, there is some influence of the perception of career perspectives (although the effect is significant, it isn’t very large): the more positive a PhD graduate is about the long-term career perspectives in academic R&D, the less likely they are to be working in non-academic R&D. Furthermore, PhDs working in non-academic R&D are more likely to be positive about the quality of HRM in academic R&D.
Main influencing factors here: the availability of permanent positions in academic R&D; the more positive, the less likely to be working in non-R&D. It is the other way around for the quality of HRM. Here, the perception of the own scientific oeuvre plays a role: the more positive, the less likely to be working in non-R&D. Important job characteristics playing a role in job choice are creativeness, intellectual challenge, the job opportunities within the organization, and personal and family-related circumstances.