scholarly journals Chimp&See: an online citizen science platform for large-scale, remote video camera trap annotation of chimpanzee behaviour, demography and individual identification

2016 ◽  
Author(s):  
Mimi Arandjelovic ◽  
Colleen R Stephens ◽  
Maureen S McCarthy ◽  
Paula Dieguez ◽  
Ammie K Kalan ◽  
...  
Keyword(s):  
Citizen Science ◽  
General Public ◽  
Large Scale ◽  
Video Camera ◽  
Camera Trap ◽  
Scientific Data ◽  
Individual Identification ◽  
Community Size ◽  
Data Set ◽  
Effective Manner

The Pan African Programme: The cultured chimpanzee (PanAf) is a large-scale research project across the chimpanzee (Pan troglodytes) range which aims to better understand and model the socioecological and demographic drivers of chimpanzee diversity. As part of the PanAf, over 350,000 1-minute camera trap videos have been recorded. To annotate this large data set and ascertain individual chimpanzee identifications from 39 different temporary and collaborative chimpanzee research sites, we developed the web-based citizen science platform Chimp&See (www.chimpandsee.org) in collaboration with the Zooniverse. Chimp&See allows members of the general public to view the PanAf videos online and annotate which species are present and the behaviours they exhibit in each video. These citizen scientists also watch and discuss videos to determine unique chimpanzee individuals and match them from different video clips. Each video is viewed by up to 15 unique users, allowing us to obtain a confidence score based on the number of consensus matches for each identification. In this poster, we compare the accuracy and efficiency achieved by the general public on this platform to automated facial detection software and expert scientific annotators. We also evaluate whether citizen science and video camera trapping is a way forward for assessing chimpanzee age/sex structure, density and community size in a cost and time effective manner. Finally, we discuss the balance between maintaining user engagement and obtaining detailed and accurate scientific data from citizen scientists.

scholarly journals Chimp&See: an online citizen science platform for large-scale, remote video camera trap annotation of chimpanzee behaviour, demography and individual identification

2016 ◽  
Author(s):  
Mimi Arandjelovic ◽  
Colleen R Stephens ◽  
Maureen S McCarthy ◽  
Paula Dieguez ◽  
Ammie K Kalan ◽  
...  
Keyword(s):  
Citizen Science ◽  
General Public ◽  
Large Scale ◽  
Video Camera ◽  
Camera Trap ◽  
Scientific Data ◽  
Individual Identification ◽  
Community Size ◽  
Data Set ◽  
Effective Manner

The Pan African Programme: The cultured chimpanzee (PanAf) is a large-scale research project across the chimpanzee (Pan troglodytes) range which aims to better understand and model the socioecological and demographic drivers of chimpanzee diversity. As part of the PanAf, over 350,000 1-minute camera trap videos have been recorded. To annotate this large data set and ascertain individual chimpanzee identifications from 39 different temporary and collaborative chimpanzee research sites, we developed the web-based citizen science platform Chimp&See (www.chimpandsee.org) in collaboration with the Zooniverse. Chimp&See allows members of the general public to view the PanAf videos online and annotate which species are present and the behaviours they exhibit in each video. These citizen scientists also watch and discuss videos to determine unique chimpanzee individuals and match them from different video clips. Each video is viewed by up to 15 unique users, allowing us to obtain a confidence score based on the number of consensus matches for each identification. In this poster, we compare the accuracy and efficiency achieved by the general public on this platform to automated facial detection software and expert scientific annotators. We also evaluate whether citizen science and video camera trapping is a way forward for assessing chimpanzee age/sex structure, density and community size in a cost and time effective manner. Finally, we discuss the balance between maintaining user engagement and obtaining detailed and accurate scientific data from citizen scientists.

Using Research Evidence to Inform Public Policy Decisions

2013 ◽  
Vol 51 (5) ◽  
pp. 412-422 ◽  
Author(s):  
Charles Moseley ◽  
Harold Kleinert ◽  
Kathleen Sheppard-Jones ◽  
Stephen Hall
Keyword(s):  
Public Policy ◽  
Large Scale ◽  
Research Evidence ◽  
State Level ◽  
Scientific Data ◽  
Policy Decisions ◽  
Data Set ◽  
Large Scale Data ◽  
Drive State ◽  
Public Policy Decisions

Abstract The application of scientific data in the development and implementation of sound public policy is a well-established practice, but there appears to be less consensus on the nature of the strategies that can and should be used to incorporate research data into policy decisions. This paper describes the promise and the challenges of using research evidence to inform public policy. Most specifically, we demonstrate how the application of a large-scale data set, the National Core Indicators (NCI), can be systematically used to drive state-level policy decisions, and we describe a case example of one state's application of NCI data to make significant changes to its Intellectual and Developmental Disabilities waiver. The need for continued research in this area is highlighted.

scholarly journals Candid Critters: Challenges and Solutions in a Large-Scale Citizen Science Camera Trap Project

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Monica Lasky ◽  
Arielle Parsons ◽  
Stephanie Schuttler ◽  
Alexandra Mash ◽  
Lincoln Larson ◽  
...  
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Camera Trap

scholarly journals PAQUETE camtrapR PARA GESTIONAR DATOS DE FOTO-TRAMPEO: APLICACIÓN EN LA RESERVA DE LA BIOSFERA TEHUACÁN-CUICATLÁN

2017 ◽  
Vol 1 (2) ◽  
pp. 13
Author(s):  
Eva López-Tello ◽  
Salvador Mandujano
Keyword(s):  
Technological Development ◽  
Biosphere Reserve ◽  
R Package ◽  
Camera Trap ◽  
Individual Identification ◽  
Data Set ◽  
Organization And Management ◽  
R Packages ◽  
Visualization Of Data ◽  
Short Time

ResumenEl empleo de cámaras trampa es un método que se ha popularizado en la última década debido al desarrollo tecnológico que ha hecho más accesible la adquisición de este equipo. Una de las ventajas de este método es que podemos obtener mucha información en poco tiempo de diferentes especies. Sin embargo, existen pocos programas que faciliten la organización y extracción de la información de una gran cantidad de imágenes. Recientemente se ha puesto disponible libremente el paquete R llamado camtrapR, el cual sirve para extraer los metadatos de las imágenes, crear tablas de registros independientes, registros de presencia/ausencia para ocupación, y gráficos espaciales. Para comprobar la funcionalidad del programa en este artículo presentamos seis ejemplos de las principales funciones de camtrapR. Para esto se utilizó un conjunto de imágenes obtenidas con 10 cámaras-trampa en una localidad de la Reserva de la Biosfera Tehuacán-Cuicatlán. camtrapR se aplicó para probar los siguientes objetivos: organización y manejo de las fotos, clasificación por especie, identificación individual, extracción de metadatos por especie y/o individuos, exploración y visualización de datos, y exportación de datos para análisis de ocupación. Está disponible libre el código R utilizado en este trabajo. De acuerdo a los resultados obtenidos se considera que camtrapR es un paquete eficiente para facilitar y reducir el tiempo de extracción de los metadatos de las imágenes; así mismo es posible obtener los registros independientes sin errores de omisión o duplicación de datos. Además, permite crear archivos *.csv que después pueden ser analizados con otros paquetes R o programas para otros propósitos.Palabras clave: base de datos, historias de captura, metadatos, R. AbstractThe camera-trap is a method that has become popular in the last decade due to the technological development that has made the acquisition of this equipment more accessible. One of the advantages of this method is that we can get a lot of information in a short time for different species. However, there are few programs that facilitate the organization and extraction of information from large number of images. Recently, the R package called camtrapR has been made freely available, which serves to extract the metadata from the images, create independent record tables, occupation presence/absence registers and spatial graphics. To check the functionality of this package, in this article we present six examples of how to use the main functions of camtrapR. For this purpose, we used a data set of images obtained with 10 cameras in the location of the Tehuacán-Cuicatlán Biosphere Reserve. camtrapR was applied to test the following objectives: organization and management of the photos, classification by species, individual identification, extraction of metadata by species and individuals, exploration and visualization of data, and export of data for analysis of occupation. The R code used in this work is available freely in line. According to our results, camtrapR is an efficient package to facilitate and reduce the extraction time of the metadata of the images; it is also possible to obtain the independent records without errors of omission or duplication of data. In addition, it allows to create * .csv files that can then be analyzed with other R packages or programs for different objectives.Key words: capture histories, database, metadata, organization, R.

scholarly journals Citizen science, computing, and conservation: How can “Crowd AI” change the way we tackle large-scale ecological challenges?

2021 ◽  
Vol 8 (2) ◽  
pp. 54-75
Author(s):  
Meredith S. Palmer ◽  
Sarah E. Huebner ◽  
Marco Willi ◽  
Lucy Fortson ◽  
Craig Packer
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Citizen Science ◽  
Large Scale ◽  
Camera Trap ◽  
Camera Traps ◽  
Machine Learning Algorithms ◽  
Anthropogenic Factors ◽  
Spatial And Temporal Scales ◽  
The Way

Camera traps - remote cameras that capture images of passing wildlife - have become a ubiquitous tool in ecology and conservation. Systematic camera trap surveys generate ‘Big Data’ across broad spatial and temporal scales, providing valuable information on environmental and anthropogenic factors affecting vulnerable wildlife populations. However, the sheer number of images amassed can quickly outpace researchers’ ability to manually extract data from these images (e.g., species identities, counts, and behaviors) in timeframes useful for making scientifically-guided conservation and management decisions. Here, we present ‘Snapshot Safari’ as a case study for merging citizen science and machine learning to rapidly generate highly accurate ecological Big Data from camera trap surveys. Snapshot Safari is a collaborative cross-continental research and conservation effort with 1500+ cameras deployed at over 40 eastern and southern Africa protected areas, generating millions of images per year. As one of the first and largest-scale camera trapping initiatives, Snapshot Safari spearheaded innovative developments in citizen science and machine learning. We highlight the advances made and discuss the issues that arose using each of these methods to annotate camera trap data. We end by describing how we combined human and machine classification methods (‘Crowd AI’) to create an efficient integrated data pipeline. Ultimately, by using a feedback loop in which humans validate machine learning predictions and machine learning algorithms are iteratively retrained on new human classifications, we can capitalize on the strengths of both methods of classification while mitigating the weaknesses. Using Crowd AI to quickly and accurately ‘unlock’ ecological Big Data for use in science and conservation is revolutionizing the way we take on critical environmental issues in the Anthropocene era.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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