1,801 research outputs found
Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior
This paper analyzes the tweeting behavior of 37 astrophysicists on Twitter
and compares their tweeting behavior with their publication behavior and
citation impact to show whether they tweet research-related topics or not.
Astrophysicists on Twitter are selected to compare their tweets with their
publications from Web of Science. Different user groups are identified based on
tweeting and publication frequency. A moderate negative correlation (p=-0.390*)
is found between the number of publications and tweets per day, while retweet
and citation rates do not correlate. The similarity between tweets and
abstracts is very low (cos=0.081). User groups show different tweeting behavior
such as retweeting and including hashtags, usernames and URLs. The study is
limited in terms of the small set of astrophysicists. Results are not
necessarily representative of the entire astrophysicist community on Twitter
and they most certainly do not apply to scientists in general. Future research
should apply the methods to a larger set of researchers and other scientific
disciplines. To a certain extent, this study helps to understand how
researchers use Twitter. The results hint at the fact that impact on Twitter
can neither be equated with nor replace traditional research impact metrics.
However, tweets and other so-called altmetrics might be able to reflect other
impact of scientists such as public outreach and science communication. To the
best of our knowledge, this is the first in-depth study comparing researchers'
tweeting activity and behavior with scientific publication output in terms of
quantity, content and impact.Comment: 14 pages, 5 figures, 7 table
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
This brief communication presents preliminary findings on automated Twitter
accounts distributing links to scientific papers deposited on the preprint
repository arXiv. It discusses the implication of the presence of such bots
from the perspective of social media metrics (altmetrics), where mentions of
scholarly documents on Twitter have been suggested as a means of measuring
impact that is both broader and timelier than citations. We present preliminary
findings that automated Twitter accounts create a considerable amount of tweets
to scientific papers and that they behave differently than common social bots,
which has critical implications for the use of raw tweet counts in research
evaluation and assessment. We discuss some definitions of Twitter cyborgs and
bots in scholarly communication and propose differentiating between different
levels of engagement from tweeting only bibliographic information to discussing
or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl
Astrophysicists’ conversational connections on Twitter
Because Twitter and other social media are increasingly used for analyses based on altmetrics, this research sought to
understand what contexts, affordance use, and social activities influence the tweeting behavior of astrophysicists. Thus, the
presented study has been guided by three research questions that consider the influence of astrophysicists’ activities (i.e.,
publishing and tweeting frequency) and of their tweet construction and affordance use (i.e. use of hashtags, language, and
emotions) on the conversational connections they have on Twitter. We found that astrophysicists communicate with a
variety of user types (e.g. colleagues, science communicators, other researchers, and educators) and that in the ego
networks of the astrophysicists clear groups consisting of users with different professional roles can be distinguished.
Interestingly, the analysis of noun phrases and hashtags showed that when the astrophysicists address the different groups
of very different professional composition they use very similar terminology, but that they do not talk to each other (i.e.
mentioning other user names in tweets). The results also showed that in those areas of the ego networks that tweeted more
the sentiment of the tweets tended to be closer to neutral, connecting frequent tweeting with information sharing activities
rather than conversations or expressing opinions
Gene regulatory networks elucidating huanglongbing disease mechanisms.
Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur
Measuring the societal impact of open science – Presentation of a research project
Research assessment has become increasingly important—especially in these economically challenging times—as funders of research try to identify researchers, research groups, and universities that are most deserving of the limited funds. The goal of any research assessment is to discover research that is of the highest quality and therefore more deserving of funding. As quality is very difficult and time-consuming to assess and can be highly subjective, other approaches have been preferred for assessment purposes (especially when assessing big data). Research assessments usually focus on evaluating the level of impact a research product has made; impact is therefore used as a proxy for quality. Impact can, however, be difficult to identify, track, and quantify, and as the current methods to assess research impact are being increasingly criticized, new methods and data sources need to be investigated. For this purpose, the altmetrics movement is investigating what the online mentions of research products can disclose about the impact research has had and who has been influenced by research. The focus of the research project presented here will be to examine online mentions of research products and to develop methods and tools to evaluate the potential of these mentions for measuring societal impact of Finnish research in Finland and beyond. This research will 1) map the current state of research in Finland using altmetric research methods and data, and 2) investigate novel quantitative indicators of research impact to incentivize researchers in adopting the open science movement.</p
On the differences between citations and altmetrics: An investigation of factors driving altmetrics vs. citations for Finnish Articles
This study examines a range of factors associating with future citation and altmetric counts to a paper. The factors include journal impact factor, individual collaboration, international collaboration, institution prestige, country prestige, research funding, abstract readability, abstract length, title length, number of cited references, field size, and field type and will be modelled in association with citation counts, Mendeley readers, Twitter posts, Facebook posts, blog posts, and news posts. The results demonstrate that eight factors are important for increased citation counts, seven different factors are important for increased Mendeley readers, eight factors are important for increased Twitter posts, three factors are important for increased Facebook posts, six factors are important for increased blog posts, and five factors are important for increased news posts. Journal impact factor and international collaboration are the two factors that significantly associate with increased citation counts and with all altmetric scores. Moreover, it seems that the factors driving Mendeley readership are similar to those driving citation counts. However, the altmetric events differ from each other in terms of a small number of factors; for instance, institution prestige and country prestige associate with increased Mendeley readers and blog and news posts, but it is an insignificant factor for Twitter and Facebook posts. The findings contribute to the continued development of theoretical models and methodological developments associated with capturing, interpreting, and understanding altmetric events. </p
A statistical interpretation of the correlation between intermediate mass fragment multiplicity and transverse energy
Multifragment emission following Xe+Au collisions at 30, 40, 50 and 60 AMeV
has been studied with multidetector systems covering nearly 4-pi in solid
angle. The correlations of both the intermediate mass fragment and light
charged particle multiplicities with the transverse energy are explored. A
comparison is made with results from a similar system, Xe+Bi at 28 AMeV. The
experimental trends are compared to statistical model predictions.Comment: 7 pages, submitted to Phys. Rev.
CitDet: A Benchmark Dataset for Citrus Fruit Detection
In this letter, we present a new dataset to advance the state of the art in
detecting citrus fruit and accurately estimate yield on trees affected by the
Huanglongbing (HLB) disease in orchard environments via imaging. Despite the
fact that significant progress has been made in solving the fruit detection
problem, the lack of publicly available datasets has complicated direct
comparison of results. For instance, citrus detection has long been of interest
in the agricultural research community, yet there is an absence of work,
particularly involving public datasets of citrus affected by HLB. To address
this issue, we enhance state-of-the-art object detection methods for use in
typical orchard settings. Concretely, we provide high-resolution images of
citrus trees located in an area known to be highly affected by HLB, along with
high-quality bounding box annotations of citrus fruit. Fruit on both the trees
and the ground are labeled to allow for identification of fruit location, which
contributes to advancements in yield estimation and potential measure of HLB
impact via fruit drop. The dataset consists of over 32,000 bounding box
annotations for fruit instances contained in 579 high-resolution images. In
summary, our contributions are the following: (i) we introduce a novel dataset
along with baseline performance benchmarks on multiple contemporary object
detection algorithms, (ii) we show the ability to accurately capture fruit
location on tree or on ground, and finally (ii) we present a correlation of our
results with yield estimations.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L
International Wildlife Law : Understanding and Enhancing Its Role in Conservation
We gratefully acknowledge valuable input by Kees Bastmeijer, Sanja Bogojevic, Jennifer Dubrulle, and Han Somsen.Peer reviewedPublisher PD
- …