AusTraits, a curated plant trait database for the Australian flora
- Authors: Falster, Daniel , Gallagher, Rachael , Wenk, Elizabeth , Wright, Ian , Cheal, David
- Date: 2021
- Type: Text , Journal article , Data article
- Relation: Scientific Data Vol. 8, no. 1 (2021), p.
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- Description: We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge. © 2021, The Author(s). **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “David Cheal" is provided in this record**
Survey data regarding perceived air quality in Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa, United States before and during Covid-19 restrictions
- Authors: Barbieri, Diego , Lou, Baowen , Passavanti, Marco , Hui, Cang , Lessa, Daniela , Maharaj, Brij , Banerjee, Arunabha , Wang, Fusong , Chang, Kevin , Naik, Bhaven , Yu, Lei , Liu, Zhuangzhuang , Sikka, Gaurav , Tucker, Andrew , Mirhosseini, Ali , Naseri, Sahra , Qiao, Yaning , Gupta, Akshay , Abbas, Montasir , Fang, Kevin , Ghasemi, Navid , Peprah, Prince , Goswami, Shubham , Hessami, Amir , Agarwal, Nithin , Lam, Louisa , Adomako, Solomon
- Date: 2020
- Type: Text , Journal article , Data article
- Relation: Data in Brief Vol. 32, (2020)
- Full Text: false
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- Description: The dataset deals with the air quality perceived by citizens before and during the enforcement of COVID-19 restrictions in ten countries around the world: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. An online survey conveniently translated into Chinese, English, Italian, Norwegian, Persian, Portuguese collected information regarding the perceived quality of air pollution according to a Likert scale. The questionnaire was distributed between 11-05-2020 and 31-05-2020 and 9 394 respondents took part. Both the survey and the dataset (stored in a Microsoft Excel Worksheet) are available in a public repository. The collected data offer the people's subjective perspectives related to the objective improvement in air quality occurred during the COVID-19 restrictions. Furthermore, the dataset can be used for research studies involving the reduction in air pollution as experienced, to a different extent, by populations of all the ten countries. © 2020 The Author(s)
Wifi-based localisation datasets for No-GPS open areas using smart bins
- Authors: Nassar, Mohamed , Hasan, Mahmud , Khan, Md , Sultana, Mirza , Hasan, Md , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2020
- Type: Text , Journal article , Data article
- Relation: Computer Networks Vol. 180, no. (2020), p. 1-5
- Full Text: false
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- Description: In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.