Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

Auto-filtering validation in citizen science biodiversity monitoring ::a case study

Data quality is the primary concern for researchers working on citizen science projects. The collected data by citizen science participants are heterogeneous and therefore must be validated. There are several validation approaches depending on the theme and objective of the citizen science project, but the most common approach is the expert review. While expert validation is essential in citizen science projects, considering it as the only validation approach can be very difficult and complicated for the experts. In addition, volunteers can get demotivated to contribute if they do not receive any feedback regarding their submissions. This project aims at introducing an automatic filtering mechanism for a biodiversity citizen science project. The goals of this project are to first use an available historical database of the local species to filter out the unusual ones, and second to use machine learning and image recognition techniques to verify if the observation image corresponds with the right species type. The submissions that does not successfully pass the automatic filtering will be flagged as unusual and goes through expert review. The objective is on the one hand to simplify validation task by the experts, and on the other hand to increase participants’ motivation by giving them real-time feedback on their submissions. Finally, the flagged observations will be classified as valid, valid but uncommon, and invalid, and the observation outliers (rare species) can be identified for each specific region

Untapped: Accessing Extension to Strengthen Connections Between Citizen Science and Community Decision Making

Citizen science is on the rise, and Extension is poised to support this movement by offering technical assistance to citizen science programs, communities, federal partners, and researchers. The expansion of citizen science provides an opportunity for fostering innovative access to Extension resources and increasing engagement with new audiences. To encourage capitalization on this opportunity, we outline Extension\u27s traditional strengths and connect them to the needs of citizen science programs, offer examples of Extension-based citizen science programs that are working with communities to make natural resource management decisions, and make suggestions for ways in which Extension\u27s technical assistance can be shared with the citizen science community to build new partnerships

Understanding information diversity in the era of repurposable crowdsourced data

Organizations successfully leverage information technology for the acquisition of knowledge for decision-making through information crowdsourcing, which is gathering information from a group of people about a phenomenon of interest to the crowdsourcer. Information crowdsourcing has been used to drive business insight and scientific research, providing crowdsourcers access to information outside their traditional reach. Crowdsourcers seek high-quality data for their information crowdsourcing projects and require contributors who can provide data that meet predetermined requirements. Crowdsourcers recruit contributors with high levels of relevant knowledge or train contributors to ensure the quality of data they collect. However, when crowdsourced data needs to fit more than a single usage scenario because the requirements of the project changed or the data needs to be repurposed for tasks other than the one(s) for which it was initially collected, the ability of contributors to provide diverse data that can meet multiple requirements is also desirable. In this thesis, I investigate how the domain knowledge a contributor possesses affects the diversity and quality of data they report. Using an experiment in which 84 students randomly assigned to three knowledge conditions reported information about artificial stimuli, I found that explicitly trained contributors provided less diverse data than either implicitly trained or untrained contributors. In addition, I looked at the longitudinal effect of knowledge on the diversity of data reported by contributors. Using review data from Amazon.com and organism sighting data from NLNature.com (a citizen science data crowdsourcing platform), I studied the impact of knowledge on the diversity and quality of crowdsourced data. The results show that experience reduced the diversity and usefulness of contributed data. The study provides insights for crowdsourcers in industry and academia on how to manage and utilize their crowds effectively to collect high-quality reusable data

Mutual learning exercise on citizen science initiatives: policy and practice. Second thematic report: ensuring good practices and impacts

This publication provides a summary of the Mutual Learning Exercise on Good Practices on Citizen Science and their Impact. This document starts by presenting the examples of successful CS national projects chosen by the 11 countries participating in the MLE, and the variables against which the projects were analysed. Chapter 2 summarises the results related to challenges & mitigation strategies with the implementation of CS projects. Chapter 3 analyses the examples of CS networks and centres of expertise and presents the current state of national funding opportunities that were provided by the 11 participating countries in the MLE. Chapter 4 provides recommendations which cover a range of potential actions targeting different aspects discussed during the workshop sessions to better implement and especially support CS initiatives and projects and overcome the detected barriers. The document concludes with Chapter 5 which briefly explains the next MLE topic sessions

OPENSENSEMAP - A Citizen Science Platform For Publishing And Exploring Sensor Data as Open Data

A plethora of citizen science sensor platforms for dierent use-cases already exist. They provide cheap, precongured, plug and playable hardware and software solutions. Using data from multiple platforms and resources can be a challenging task in respect of discovering, exploring, downloading and converting. This work aims to implement a one-stop-shop for sensor data that tries to tackle these problems. Therefore a basic data schema capable of metadata is established that allows publishing generic sensor platforms and sensor data. For exploration a web platform is implemented based on common web standards. This work describes the requirements, design and implementation of the OpenSenseMap. OpenSenseMap is following the clientserver model and its complete application stack is implemented in JavaScript. It describes the basic components and the underlying data model. All components are using open-source technology and are published under an opensource license too. The RESTful service is the heart of OpenSenseMap and implements the data publish and data retrieval functions. Developers may use it to build custom applications around OpenSenseMap or integrate OpenSenseMap into existing projects. The NodeJS application runs its own web server and implements the different HTTP request types. The OpenSenseMap concept is being evaluated with a technical and a user survey

Evaluating the role of citizen science in biological investigations

Considering recent and predicted ecological changes (caused by global climate change), baseline monitoring of biological diversity becomes an extremely useful record to have. Citizen science provides a unique, low-cost, high output method of attaining large data sets which has already been implemented in several studies. They are used by major bodies such as the Natural History Museum, OPAL and the RSPB. Despite this concerns lie with the accuracy of the data – can volunteers really produce real data? In this study various aspects were investigated, firstly the current public opinion about citizen science, how we can train volunteers carrying out surveys, and the kinds of method suitable for biological monitoring by citizen scientists. This report finds a generally good level of public literacy in terms of the existence and the potential of, citizen science. Participant confidence significantly increases once a volunteer has carried out a survey – but this does not show any relationship with accuracy. It is concluded that estimation of abundance must be embedded with training and validation methods, and it is recommended further work is carried out into robust online training and validation

A Case Study of Librarian Outreach to Scientists: Collaborative Research and Scholarly Communication in Conservation Biology

Global collaboration is increasingly important across universities and conservation biology organizations. In this example, a partnership resulted in the creation of a short-course aimed at exploring communication forms and digital tools that facilitate scholarly communication in conservation biology. Questions the authors hoped to answer in the course were: What are the benefits and limitations of these tools? How can researchers in conservation biology use these methods to share data, show impact and connect to colleagues and stakeholders? Why is open access even more important in this highly collaborative scholarly environment? How can communities of interest benefit scientists and the public

Citizen science and the United Nations Sustainable Development Goals

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution