scholarly journals Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks

2020 ◽  
Vol 12 (12) ◽  
pp. 210
Author(s):  
Suvodeep Mazumdar ◽  
Dhavalkumar Thakker
Keyword(s):  
Social Media ◽  
Citizen Science ◽  
Large Scale ◽  
Term Study ◽  
The Public ◽  
Long Term Study ◽  
Science Projects ◽  
Using Data ◽  
Science Activities

This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public with insights around how social media can be used to share citizen science related topics, particularly to help increase visibility, influence change and in general and raise awareness on topics. To the knowledge of the authors, this is the first large-scale study on understanding how such information is discussed on Twitter, particularly outside the scope of individual projects. The paper reports on the wide variety of topics (e.g., politics, news, ecological observations) being discussed on social media and a wide variety of network types and the varied roles played by users in sharing information in Twitter. Based on these findings, the paper highlights recommendations for stakeholders for engaging with citizen science topics.

scholarly journals Comparison of large‐scale citizen science data and long‐term study data for phenology modeling

Ecology ◽  
2018 ◽  
Vol 100 (2) ◽  
pp. e02568 ◽  
Author(s):  
Shawn D. Taylor ◽  
Joan M. Meiners ◽  
Kristina Riemer ◽  
Michael C. Orr ◽  
Ethan P. White
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Study Data ◽  
Science Data ◽  
Term Study ◽  
Long Term Study

scholarly journals Comparison of large-scale citizen science data and long-term study data for phenology modeling

2018 ◽  
Author(s):  
Shawn D. Taylor ◽  
Joan M. Meiners ◽  
Kristina Riemer ◽  
Michael C. Orr ◽  
Ethan P. White
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Parameter Estimates ◽  
Similar Data ◽  
Annual Variability ◽  
Long Term Study ◽  
Out Of Sample ◽  
Using Data ◽  
Simple Models

AbstractLarge-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and inter-annual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science National Phenology Network (NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built process based phenology models for species common to each dataset. From these models we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two datasets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for NPN data had the lowest error when using models built from the NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. Accordingly, the choice of dataset depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale NPN data, which has high spatial representation and a large regional species pool. LTER datasets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models which integrate the strengths of both datasets.

The Relationship between Suicide and Five Climate Issues in a Large-scale and Long Term Study in Japan

2012 ◽  
Vol 61 (5) ◽  
pp. 532-537 ◽  
Author(s):  
K Inoue ◽  
Y Nishimura ◽  
Y Fujita ◽  
Y Ono ◽  
T Fukunaga
Keyword(s):  
Large Scale ◽  
Term Study ◽  
Long Term Study ◽  
The Relationship

Anticarcinogenic Impact of Interferon on Patients with Chronic Hepatitis C: A Large-Scale Long-Term Study in a Single Center

Intervirology ◽  
2005 ◽  
Vol 49 (1-2) ◽  
pp. 82-90 ◽  
Author(s):  
Kenji Ikeda ◽  
Yasuji Arase ◽  
Satoshi Saitoh ◽  
Masahiro Kobayashi ◽  
Takashi Someya ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Chronic Hepatitis C ◽  
Large Scale ◽  
Single Center ◽  
Term Study ◽  
Long Term Study

Advances in Documentation of Commingled and Fragmentary Remains

2019 ◽  
Vol 7 (1) ◽  
pp. 77-86 ◽  
Author(s):  
Anna J. Osterholtz
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Recording System ◽  
Sex Estimation ◽  
Minimal Set ◽  
Term Study ◽  
Long Term Study ◽  
User Friendly ◽  
Meaningful Data

AbstractCommingled and fragmentary remains are found in numerous contexts worldwide. These assemblages typically require large scale, long term study to fully extract and contextualize meaningful data. However, when uncovered in CRM and foreign settings where remains cannot leave their country of origin, there is a need for quick, reliable data collection. Presented here is a recording system for use in field- and research-based laboratory settings. Utilizing visual forms and a minimal set of observations for skeletal elements from the cranium to the foot, the database facilitates data collection of fragment identification, age at death and sex estimation, dental observations, trauma recording, and taphonomic observations. A data dictionary is also provided, with definitions and value lists used in the database itself. The database has been used in field labs throughout the old world and by numerous researchers who have modified it to meet their own research needs. By presenting a minimal standard of data in a highly adaptable database, the recording system described here provides consistent baseline data in a user-friendly, quick-access format

Efficient biogas production from cattle manure in a plug flow reactor: A large scale long term study

2019 ◽  
Vol 278 ◽  
pp. 450-455 ◽  
Author(s):  
Lili Dong ◽  
Guangli Cao ◽  
Xianzhang Guo ◽  
Tianshu Liu ◽  
Jiwen Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Flow Reactor ◽  
Biogas Production ◽  
Plug Flow ◽  
Cattle Manure ◽  
Plug Flow Reactor ◽  
Term Study ◽  
Long Term Study

scholarly journals Aves, Booderee National Park, Jervis Bay territory, south-eastern Australia

Check List ◽  
2009 ◽  
Vol 5 (3) ◽  
pp. 479 ◽  
Author(s):  
David B. Lindenmayer ◽  
Christopher MacGregor ◽  
Darren Brown ◽  
Rebecca Montague-Drake ◽  
Mason Crane ◽  
...  
Keyword(s):  
National Park ◽  
Large Scale ◽  
Individual Species ◽  
Vegetation Types ◽  
Term Study ◽  
South Eastern ◽  
Long Term Study ◽  
Eastern Australia ◽  
South Eastern Australia

A large-scale, long-term study is being conducted to describe the bird assemblages inhabiting a 6500 ha area at Booderee National Park, south-eastern Australia. In this paper, we provide a list of birds recorded within rainforest, forest, woodland, shrubland, heathland and sedgeland during surveys conducted each spring between 2003 and 2007. Of particular interest was the contrast between the birds of sites burned in a wildfire in 2003 and sites that remained unburned. We recorded a total of 103 species from 35 families. We found that after the major fire, the vast majority of individual species and the bird assemblage per se in most vegetation types recovered within two years. Exceptions occurred in structurally simple vegetation types such as sedgeland and wet heathland in which reduced levels of species had not returned to pre-fire (2003) levels by 2007.

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