scholarly journals Bhutan’s Challenges in Biodiversity Informatics

2019 ◽  
Vol 3 ◽  
Author(s):  
Choki Gyeltshen ◽  
Sangay Dema
Keyword(s):  
Protected Areas ◽  
Coordination Mechanism ◽  
Easy Access ◽  
Observation Data ◽  
Biodiversity Data ◽  
User Friendliness ◽  
Or Education ◽  
Web Based ◽  
The Status ◽  
Biodiversity Information

Access to reliable and updated data and information on the status of biodiversity for effective conservation and sustainable use has been one of the major challenges in Bhutan. The current scenario of inaccessibility is due to the fact that biodiversity inventories and documentation are carried out within the context of individual projects and institutions, guided by their specific objectives and collection standards, often in isolation. More critical is the fact that these data hardly get shared nor are they easily accessible, resulting in either duplication of efforts or underutilization of the existing data. It has been duly noted that despite the global recognition of Bhutan’s protected areas system and its conservation achievements, information on the existing biodiversity of these protected areas is not easily accessible. There is also inadequate information on the critical biodiverse areas of the country, making it difficult to make informed decisions for either initiating developmental activities or prioritizing the area for conservation. These gaps are acknowledged and discussed in national documents (NBSAP 2014). In order to provide easy access to comprehensive biodiversity data and information of the country and to ensure the judicious use of our scarce resources, there is a compelling need to establish a coordination mechanism for sharing data on a common platform, not only to overcome the existing gaps but also to enable consolidation and analysis of the data in order to generate information for broader use such as conservation planning or education. Thus in 1994, Bhutan, along with the South-South Cooperation (PSC 2009), which included Benin and Costa Rica, initiated a basic biodiversity information system in each country, funded by the Kingdom of Netherlands. In 2008, the National Biodiversity Centre (NBC) developed a web-based biodiversity portal, which was subsequently upgraded to the status of a national biodiversity information clearing house in 2010. However, because of the vastness and variety of biodiversity data, it was not feasible for a single agency to collect as well as curate these vast data. Thus, in early 2013, the Centre proposed the formation of a consortium to manage biodiversity data through a strengthened and an improved version of a web-based portal. In addition, this initiative to form a consortium amongst different biodiversity stakeholders, was also to address the issue of duplicative efforts in developing and managing isolated information systems and databases. The Bhutan Biodiversity Portal (www.biodiversity.bt) was launched on 17th December 2013. Currently, the observation data has crossed 63,000 of all taxa owing mostly to the efforts of a mass campaign across the country. However, one of the major challenges is the availability of active taxonmic curators especially for the understudied taxonomic groups such as invertebrates. In addition, some users prefer social media over the portal due to its user-friendliness.

scholarly journals Contribution of Citizen Science to Biodiversity Data Mobilization in Russia

2020 ◽  
Vol 4 ◽  
Author(s):  
Natalya Ivanova ◽  
Maxim Shashkov
Keyword(s):  
New Species ◽  
Protected Areas ◽  
Citizen Science ◽  
Biodiversity Data ◽  
Breeding Bird ◽  
Breeding Bird Atlas ◽  
Research Grade ◽  
Taxonomic Groups ◽  
Species Occurrences ◽  
Biodiversity Information

Currently Russia doesn't have a national biodiversity information system, and is still not a GBIF (Global Biodiversity Information Facility) member. Nevertheless, GBIF is the largest source of biodiversity data for Russia. As of August 2020, >5M species occurrences were available through the GBIF portal, of which 54% were published by Russian organisations. There are 107 institutions from Russia that have become GBIF publishers and 357 datasets have been published. The important trend of data mobilization in Russia is driven by the considerable contribution of citizen science. The most popular platform is iNaturalist. This year, the related GBIF dataset (Ueda 2020) became the largest one for Russia (793,049 species occurrences as of 2020-08-11). The first observation for Russia was posted in 2011, but iNaturalist started becoming popular in 2017. That year, 88 observers added >4500 observations that represented 1390 new species for Russia, 7- and 2-fold more respectively, than for the previous 6 years. Now we have nearly 12,000 observers, about 15,000 observed species and >1M research-grade observations. The ratio of observations for Tracheophyta, Chordata, and Arthropoda in Russia is different compared to the global scale. There are almost an equal amount of observations in the global iNaturalist GBIF dataset for these groups. At the same time in Russia, vascular plants make up 2/3rds of the observations. That is due to the "Flora of Russia" project, which attracted many professional botanists both as observers and experts. Thanks to their activity, Russia has a high proportion of research-grade observations in iNaturalist, 78% versus 60% globally. Another consequence of wide participation by professional researchers is the high rate of species accumulation. For some taxonomic groups conspicuous species were already revealed. There are about 850 bird species in Russia of which 398 species were observed in 2018, and only 83 new species in 2019. Currently, the number of new species recorded over time is decreasing despite the increase in observers and overall user activity. Russian iNaturalist observers have shared a lot of archive photos (taken during past years). In 2018, it was nearly 1/4 of the total number of observations and about 3/4 of new species for the year, with similar trends observed during 2019. Usually archive photos are posted from December until April, but the 2020 pandemic lockdown spurred a new wave of archive photo mobilisation in April and May. There are many iNaturalist projects for protected areas in Russia: 27 for strict nature reserves and national parks, and about 300 for others. About 100,000 observations (7.5% of all Russian observations) from the umbrella project "Protected areas of Russia" represent >34% of the species diversity observed in Russia. For some regions, e.g., Novosibirsk, Nizhniy Novgorod and Vladimir Oblasts, almost all protected areas are covered by iNaturalist projects, and are often their only source of available biodiversity data. There are also other popular citizen science platforms developed by Russian researchers. The first one is the Russian birdwatching network RU-BIRDS.RU. The related GBIF dataset (Ukolov et al. 2019) is the third largest dataset for Russia (>370,000 species occurrences). Another Russian citizen science system is wildlifemonitoring.ru, which includes thematic resources for different taxonomic groups of vertebrates. This is the crowd-sourced web-GIS maintained by the Siberian Environmental Center NGO in Novosibirsk. It is noteworthy that iNaturalist activities in Russia are developed more as a social network than as a way to attract volunteers to participate in scientific research. Of 746 citations in the iNaturalist dataset, only 18 articles include co-authors from Russia. iNaturalist data are used for the management of regional red lists (in the Republic of Bashkortostan, Novosibirsk Oblast and others), and as an additional information source for regional inventories. RU-BIRDS data were used in the European Russia Breeding Bird Atlas and the new edition of the European Breeding Bird Atlas. In Russia, citizen science activities significantly contribute to filling gaps in the global biodiversity map. However, Russian iNaturalist observations available through GBIF originate from the USA. It is not ideal, because the iNaturalist GBIF dataset is growing rapidly, and in the future it will represent more than all other datasets for Russia combined. In our opinion, iNaturalist data should be repatriated during the process of publishing through GBIF, as it is implemented for the eBird dataset (Levatich and Ligocki 2020).

scholarly journals CALCULUS LEARNING VIA SCREENCAST-O-MATIC DURING THE PANDEMIC: AN EXPLORATION TOWARDS STUDENTS’ PERCEPTION OF MATH ANXIETY

MaPan ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 260
Author(s):  
Robert Harry Soesanto ◽  
Kurnia Putri Sepdikasari Dirgantoro
Keyword(s):  
Mathematics Anxiety ◽  
Math Anxiety ◽  
Visual Media ◽  
Learning System ◽  
Easy Access ◽  
User Friendliness ◽  
Web Based ◽  
Online Mode ◽  
Use Of Technology ◽  
The University

The emergence of the Covid-19 pandemic has transformed the learning system into an online mode, where students' anxiety became the spotlight for educators to create a learning environment that actively engages them. Many educators are competing in maximizing the use of technology to ensure students are engaged in the learning process. The study aims to explore students’ perception of math anxiety through the implementation of Screencast-O-Matic in calculus learning. This study involves 41 students at the university level and is conducted in mixed methods, using statistics and narrative descriptives. Questionnaires and open-ended questions are used to investigate students’ testimonies about the video made by Screencast-O-Matic and their perceptions of math anxiety after learning with the video given. The results showed that the implementation of Screencast-O-Matic video gradually reduces students’ mathematics anxiety during online calculus learning. Screencast-O-Matic offers educators the opportunity to build a web-based lecture learning system in audio-visual media to minimize students' anxiety levels. Furthermore, the most important things to consider while creating an educational video are clarity of message, user-friendliness, good visualization, and easy access to students anytime and anywhere.

scholarly journals APPLICATION GIS AND WEBGIS TECHNOLOGY FOR BIODIVERSITY INFORMATION MANAGEMENT A CASE STUDY IN XUAN LIEN NATURE RESERVE

2016 ◽  
Vol 54 (4) ◽  
pp. 460
Author(s):  
Ha Quy Quynh ◽  
Dang Huy Phuong ◽  
Nguyen Tien Phuong
Keyword(s):  
Protected Areas ◽  
National Parks ◽  
Spatial Data ◽  
Nature Reserve ◽  
Biological Database ◽  
Biodiversity Data ◽  
Mammal Species ◽  
Zonation Map ◽  
The Status ◽  
Biodiversity Database

Resource management in National parks (NP), Nature reserve (NR) aim to hold the status of biological resources of the area [4]. Up to now, Vietnam has established 31 NP and about 100 NR. Most of NR, NP was completed build biological database [4]. The traditional information sharing method has showed the limitations, causing many difficulties for the users, particularly when exploring the map information. WebGIS technology developed quickly with the functions included: internet access, query spatial data in the Internet, which has promoted the possibility aid, consult spatial data, link between tables and map [1, 5, 7]. Biodiversity Database of Xuan Lien NR was built based on tables and maps. The table database is designed as relational data, including the information and records. With animals is Class - Order - Family – Species and plants is Plant - Phylum - Family - Species. The code of the species included 4 parts. One part of character and 3 parts of numbers. The database are the records of 1142 plant species, 55 mammal species, 190 birds, 38 reptiles and 35 amphibians species. The map database include base map, zonation map, infrastructure maps, vegetation and distribution map of species in protected areas. Display Biodiversity data of NR by MapServer, showing base map, zonation map, infrastructure maps, vegetation and habitat map. Using the combined technologies of Remote Sensing, GIS and WebGIS to manage, display, sharing biodiversity data of NR promotion optimization capabilities in data analyses and combined tables with map. This technology may apply for management biodiversity database of all protected areas in Vietnam.

scholarly journals FinBIF: An all-embracing, integrated, cross-sectoral biodiversity data infrastructure

2019 ◽  
Vol 3 ◽  
Author(s):  
Leif Schulman ◽  
Aino Juslén ◽  
Kari Lahti
Keyword(s):  
Natural History ◽  
Data Management ◽  
Species Identification ◽  
Large Scale ◽  
Dna Barcode ◽  
National Research Council ◽  
Observation Data ◽  
Biodiversity Data ◽  
Research Infrastructures ◽  
Biodiversity Information

The service model of the Global Biodiversity Information Facility (GBIF) is being implemented in an increasing number of national biodiversity (BD) data services. While GBIF already shares >109 data points, national initiatives are an essential component: increase in GBIF-mediated data relies on national data mobilisation and GBIF is not optimised to support local use. The Finnish Biodiversity Information Facility (FinBIF), initiated in 2012 and operational since late 2016, is one of the more recent examples of national BD research infrastructures (RIs) – and arguably among the most comprehensive. Here, we describe FinBIF’s development and service integration, and provide a model approach for the construction of all-inclusive national BD RIs. FinBIF integrates a wide array of BD RI approaches under the same umbrella. These include large-scale and multi-technology digitisation of natural history collections; building a national DNA barcode reference library and linking it to species occurrence data; citizen science platforms enabling recording, managing and sharing of observation data; management and sharing of restricted data among authorities; community-driven species identification support; an e-learning environment for species identification; and IUCN Red Listing (Fig. 1). FinBIF’s aims are to accelerate digitisation, mobilisation, and distribution of biodiversity data and to boost their use in research and education, environmental administration, and the private sector. The core functionalities of FinBIF were built in a 3.5-year project (01/2015–06/2018) by a consortium of four university-based natural history collection facilities led by the Finnish Museum of Natural History Luomus. Close to 30% of the total funding was granted through the Finnish Research Infrastructures programme (FIRI) governed by the national research council and based on scientific excellence. Government funds for productivity enhancement in state administration covered c.40 % of the development and the rest was self-financed by the implementing consortium of organisations that have both a research and an education mission. The cross-sectoral scope of FinBIF has led to rapid uptake and a broad user base of its functionalities and services. Not only researchers but also administrative authorities, various enterprises and a large number of private citizens show a significant interest in the RI (Table 1). FinBIF is now in its second construction cycle (2019–2022), funded through the FIRI programme and, thus, focused on researcher services. The work programme includes integration of tools for data management in ecological restoration and e-Lab tools for spatial analyses, morphometric analysis of 3D images, species identification from sound recordings, and metagenomics analyses.

scholarly journals Finnish Biodiversity Information Facility - Improving the Taxonomic Coverage and Deepening the Information Content Through Collaboration

2019 ◽  
Vol 3 ◽  
Author(s):  
Kari Lahti ◽  
Liselott Skarp
Keyword(s):  
Species Identification ◽  
Large Scale ◽  
Knowledge Exchange ◽  
Dna Barcode ◽  
Similar Species ◽  
Observation Data ◽  
Distributed Information ◽  
The Status ◽  
High Level ◽  
Biodiversity Information

The Finnish Biodiversity Information Facility FinBIF (LINK: species.fi), operational since late 2016, is one of the more recent examples of comprehensive, all-inclusive national biodiversity research infrastructures. FinBIF integrates a wide array of biodiversity information approaches under the same umbrella. These include species information Fig. 1 (e.g. descriptions, photos and administrative attributes); citizen science platforms enabling recording, managing and sharing of observation data; an e-learning environment for species identification; management and sharing of restricted data among authorities; building a national DNA barcode reference library and linking it to species occurrence data; community-driven species identification support; large-scale and multi-technology digitisation of natural history collections; and IUCN Red Listing to conduct a periodic national assesment of the status of the threatened species. To improve the taxonomic coverage and the content of species information, FinBIF is starting a process to collaborate with the species information community at large, in order to collate already existing but not yet openly distributed information. This also means digitisation of information from analogue sources. In addition, the attempt is to join forces with Scandinavian counterparts, namely Artdatabanken (LINK: https://www.artdatabanken.se/) and Artsdatabanken (LINK: https://www.artsdatabanken.no/), for more efficient knowledge exchange within the countries sharing the same biogeographical region and thus similar species composition. The aim is also to reach politically high level agreement for deeper and wider commitment to collaborate in compiling, digitising and sharing relevant biodiversity information over the national borders.

Investigating the Efficacy and Cost-Effectiveness of a Web-based Self-help Program for People With Adjustment Problems After an Accident (SelFIT): Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Julia Hegy ◽  
Noemi Anja Brog ◽  
Thomas Berger ◽  
Hansjoerg Znoj
Keyword(s):  
Health Care ◽  
Psychological Distress ◽  
Cost Effectiveness ◽  
Well Being ◽  
User Friendliness ◽  
Adjustment Problems ◽  
Health Care Consumption ◽  
Web Based ◽  
Self Help ◽  
Wide Range

BACKGROUND Accidents and the resulting injuries are one of the world’s biggest health care issues often causing long-term effects on psychological and physical health. With regard to psychological consequences, accidents can cause a wide range of burdens including adjustment problems. Although adjustment problems are among the most frequent mental health problems, there are few specific interventions available. The newly developed program SelFIT aims to remedy this situation by offering a low-threshold web-based self-help intervention for psychological distress after an accident. OBJECTIVE The overall aim is to evaluate the efficacy and cost-effectiveness of the SelFIT program plus care as usual (CAU) compared to only care as usual. Furthermore, the program’s user friendliness, acceptance and adherence are assessed. We expect that the use of SelFIT is associated with a greater reduction in psychological distress, greater improvement in mental and physical well-being, and greater cost-effectiveness compared to CAU. METHODS Adults (n=240) showing adjustment problems due to an accident they experienced between 2 weeks and 2 years before entering the study will be randomized. Participants in the intervention group receive direct access to SelFIT. The control group receives access to the program after 12 weeks. There are 6 measurement points for both groups (baseline as well as after 4, 8, 12, 24 and 36 weeks). The main outcome is a reduction in anxiety, depression and stress symptoms that indicate adjustment problems. Secondary outcomes include well-being, optimism, embitterment, self-esteem, self-efficacy, emotion regulation, pain, costs of health care consumption and productivity loss as well as the program’s adherence, acceptance and user-friendliness. RESULTS Recruitment started in December 2019 and is ongoing. CONCLUSIONS To the best of our knowledge, this is the first study examining a web-based self-help program designed to treat adjustment problems resulting from an accident. If effective, the program could complement the still limited offer of secondary and tertiary psychological prevention after an accident. CLINICALTRIAL ClinicalTrials.gov NCT03785912; https://clinicaltrials.gov/ct2/show/NCT03785912?cond=NCT03785912&draw=2&rank=1

Usability of Web-Based Design and Manufacturing Systems: Case Study of Micro Machining

2005 ◽  
Author(s):  
Hyung-Jung Kim ◽  
Won-Shik Chu ◽  
Hyuk-Jin Kang ◽  
Sung-Hoon Ahn ◽  
Dong-Soo Kim ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Processing Time ◽  
Micro Machining ◽  
User Friendliness ◽  
Web Based ◽  
Design And Manufacturing ◽  
Cad Cam ◽  
Machining System ◽  
Web Based System ◽  
The Web

In this paper, web-based design and manufacturing systems are compared with a commercial CAD/CAM system from the point of usability. The web-based systems included in this study were MIcro Machining System (MIMS) and SmartFab. In the MIMS architecture, a 3D model in STL format was read via a web browser, sent to the web server for toolpath planning, and NC codes were generated to be fed back to the designer through the web connection. In the SmartFab system, SolidWorks was used as the design interface with provided modified menus for micro machining. These additional menus were created by SolidWorks API that also provided web-based links to the toolpath planner. In the commercial CAD/CAM case, without using any web connection, SolidWorks or CATIA was used for design, and PowerMill was used as a CAM tool. For each design and manufacturing system, accessibility, user-friendliness, toolpath-reliability, and processing time were compared. Total 91 students tested these systems in undergraduate CAD class, and the feedback showed better performance of the web-based system in accessibility, user-friendliness, and processing time. However, reliability of the web-based system showed necessity of further improvement.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

scholarly journals Methods for Structural Stress Determination according to EN 13445-3 Annex NA – Comparison with other Codes for Unfired Pressure Vessels

2018 ◽  
Vol 165 ◽  
pp. 10003
Author(s):  
Ralf Trieglaff ◽  
Jürgen Rudolph ◽  
Martin Beckert ◽  
Daniel Friers
Keyword(s):  
Pressure Vessel ◽  
Pressure Vessels ◽  
Structural Stress ◽  
Stress Determination ◽  
User Friendliness ◽  
Fatigue Assessment ◽  
Shell Elements ◽  
European Working ◽  
The Status

The European Pressure Vessel Standard EN 13445 provides in its part 3 (Design) a simplified method (Clause 17) and a detailed method for fatigue assessment (Clause 18). Clause 18 “Detailed Assessment of Fatigue Life” is under revision within the framework of the European working group CEN/TC 54/WG 53 – Design methods with the aim of reaching a significant increase in user-friendliness and a clear guideline for the application. This paper is focused on the new informative annex NA ”Instructions for structural stress oriented finite elements analyses using brick and shell elements”. As an essential amendment for the practical user, the determination of structural stress ranges for fatigue assessment of welds is further specified in this new annex. Different application methods for the determination of structural stresses are explained in connection with the requirements for finite element models and analyses. This paper will give a short overview of the proposed approaches of structural stress determination in annex NA of the revised EN 13445-3. It will present the status of the approaches based on the results of fatigue analyses according to EN 13445-3 Clause 18 for different application examples. For verification purposes, the results of the approaches proposed in EN 13445-3 are compared with the results of other pressure vessel design codes for nuclear and non-nuclear application.

scholarly journals Developing Digital Botanical Resources for Pakistan

2019 ◽  
Vol 3 ◽  
Author(s):  
Mary Barkworth ◽  
Mushtaq Ahmad ◽  
Mudassir Asrar ◽  
Raza Bhatti ◽  
Neil Cobb ◽  
...  
Keyword(s):  
Community Of Practice ◽  
Data Use ◽  
Easy Access ◽  
Biodiversity Informatics ◽  
Biodiversity Data ◽  
High Quality ◽  
Research Activities ◽  
The World ◽  
Biodiversity Education ◽  
Using Data

In 2017, funding from the Biodiversity Information Fund for Asia accelerated data mobilization and georeferencing by Pakistani herbaria. The funding directly benefited only two herbaria but, by the end of the project 9 herbaria were involved in sharing data, 2 through GBIF (ISL 2019, SINDH 2019; codes according to Index herbariorum) and 6 others (BANNU 2019, BGH 2019, PUP 2019, QUETTA 2019, RAW 2019, SWAT 2019) through OpenHerbarium, a Symbiota based network. Eventually, all collections in OpenHerbarium are expected to become GBIF data providers. Additional Pakistani herbaria are being introduced to data mobilization and several individuals have expressed interest in learning to use OpenHerbarium to generated documented checklists for teaching and research and others for learning to link information in OpenHerbarium to other resources. These are the first steps to developing a “a large group of individuals … to train, mentor, and champion [biodiversity] data use” in Pakistan, but it is important to remember that good bioidiversity data starts in the field. We need to provide today’s collectors and educators with easy access to a) information about what constitutes a high-quality herbarium specimen; b) tools for making it easier to record and provide high quality specimen data; c) simple mechanisms for sharing data in ways that provide immediately useful resources; and d) learning to make use of the data becoming available. OpenHerbarium addresses the third and fourth needs and also makes it simple for collections to become GBIF data providers. This year, the focus will be on first two of the three steps identified. Introduction of the new resources will be used to introduce collectors and educators to the ideas underlying provision of biodiversity data that is fit for use and reuse. When Symbiota2 is functional, OpenHerbarium will be moved to that system. This will encourage development of additional tools for using biodiversity data. All these activities are essential to helping spread understanding of the concepts integral to biodiversity informatics. It is, of course, possible “to train, build, and champion data use” using data for other parts of the world, or provided by institutions from other parts of the world, but embedding good biodiversity data practices into the fabric of a country’s biodiversity education and research activities better benefits the country if a substantial portion of the data is generated from within the country. It also helps to spread knowledge of the country’s biodiversity among its students. Consequently, our focus in developing Pakistan’s capacity in biodiversity informatics is on engaging collections and collectors in sharing biodiversity data, then helping them discover, use, and create methods for developing the insights needed to encourage wise use of the country’s biological resources, and encouraging interaction. This will lead to a “community of practice” within Pakistan that can both benefit from and contribute to an international “community of practice”.

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