Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

Reporting back environmental exposure data and free choice learning.

Reporting data back to study participants is increasingly being integrated into exposure and biomonitoring studies. Informal science learning opportunities are valuable in environmental health literacy efforts and report back efforts are filling an important gap in these efforts. Using the University of Arizona's Metals Exposure Study in Homes, this commentary reflects on how community-engaged exposure assessment studies, partnered with data report back efforts are providing a new informal education setting and stimulating free-choice learning. Participants are capitalizing on participating in research and leveraging their research experience to meet personal and community environmental health literacy goals. Observations from report back activities conducted in a mining community support the idea that reporting back biomonitoring data reinforces free-choice learning and this activity can lead to improvements in environmental health literacy. By linking the field of informal science education to the environmental health literacy concepts, this commentary demonstrates how reporting data back to participants is tapping into what an individual is intrinsically motivated to learn and how these efforts are successfully responding to community-identified education and research needs

Land and cryosphere products from Suomi NPP VIIRS: overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS

Climate change and environmental data science study

Aquesta tesi pretén demostrar que canvis ambientals i climàtiques són reals mitjançant l’ús de tècniques d’aprenentatge automàtic per analitzar les dades. També fa ús del preprocessament i de l’anàlisi de sèries temporals. Als efectes d'aquesta dissertació, es van triar 50 països aleatoris amb diverses variables climàtiques i ambientals en un període de 26 anys. L'anàlisi de dades exploratòries va ajudar a visualitzar i comprendre millor els canvis en temes específics i la tendència general de cada país i del món. Aquests temes són la contaminació atmosfèrica, els gasos d’efecte hivernacle, el canvi climàtic i la cobertura del sòl. El clúster K-Means dels països va mostrar la dependència entre les variables i va ajudar a agrupar els països en 3 classes, cadascuna representant un nivell específic de sostenibilitat. Posteriorment, mitjançant l'ús de les regles d'associació, específicament l'Algoritme Apriori, es van descobrir algunes associacions ocultes entre variables com les emissions de CO2 per càpita i l'Índex de Desenvolupament Humà, el nivell de sostenibilitat del país i l'Índex de Desenvolupament Humà o la dominància de la terra (de creació humana o natural) i les emissions de CO2 per càpita. La tècnica de predicció de Regressió Lineal Múltiple es va utilitzar per calcular la mitjana d’emissions de CO2 al món el 2050, basant-se en alguns factors com l’Índex de Desenvolupament Humà, l’índex de Qualitat de l’Aire i el percentatge de dominació de la terra feta per humans. Els resultats han demostrat que els canvis climàtics i ambientals han evolucionat al llarg dels anys. Les dades van ajudar a entendre com de gran és el problema. Els resultats generals han estat millor del que s’esperava, sembla que alguns països han fet un gran esforç per millorar les seves polítiques mediambientals. Tot i així, encara hi ha alguns països (com la Xina i els EUA) que necessiten treballar en aquestes polítiques.Esta tesis pretende demostrar que cambios climaticos y ambientales son reales mediante el uso de técnicas de aprendizaje automático para analizar los datos. También hace uso de preprocesamiento y análisis de series de tiempo. Para el propósito de esta tesis, se eligieron 50 países al azar con diversas variables climáticas y ambientales durante un período de tiempo de 26 años. El análisis de datos exploratorios ayudó a visualizar y comprender mejor los cambios en temas específicos y la tendencia general de cada país y del mundo. Estos temas son la contaminación del aire, los gases de efecto invernadero, el cambio climático y la cobertura del suelo. La agrupación K-Means de países mostró la dependencia entre las variables y ayudó a agrupar a los países en 3 clases, cada una de las cuales representa un nivel específico de sostenibilidad. Posteriormente, mediante el uso de las reglas de asociación, específicamente el Algoritmo Apriori, se descubrieron algunas asociaciones ocultas entre variables como las emisiones de CO2 per cápita y el Índice de Desarrollo Humano, el nivel de sostenibilidad del país y el Índice de Desarrollo Humano o el dominio de la tierra (humana o natural) y las emisiones de CO2 per cápita. Se utilizó la técnica de pronóstico Regresión Lineal Múltiple para calcular el promedio de las emisiones de CO2 en el mundo en 2050, con base en algunos factores como el Índice de Desarrollo Humano, el Índice de Calidad del Aire y el porcentaje de dominio de la tierra creado por el hombre. Los resultados han demostrado que los cambios climáticos y ambientales han evolucionado a lo largo de los años. Los datos ayudaron a entender cómo de grande es el problema. Los resultados generales han sido mejor de lo esperado, parece que algunos países han hecho un gran esfuerzo para mejorar sus políticas medioambientales. Sin embargo, todavía hay algunos países (como la China y los EEUU) que que necesitan trabajar en estas políticas.This master’s thesis seeks to prove that climate and environmental changes are real by using machine learning techniques to analyze the data. It makes use of preprocessing and time series analysis as well. For the purpose of this dissertation, 50 random countries were chosen with various climatic and environmental variables over a time span of 26 years. Exploratory data analysis helped to visualize and better understand the changes in specific topics and the overall trend for each country and for the world. These topics are air pollution, greenhouse gases, climate change and land cover. K-Means Clustering of the countries showed the dependence between the variables and helped group countries into 3 classes, each representing a specific level of sustainability. Later, by using the association rules, specifically the Apriori Algorithm, some hidden associations were discovered among variables such as the CO2 emissions per capita and Human Development Index, the country's sustainability level and Human Development Index or the land dominance (human-made or natural) and the CO2 emissions per capita. The forecasting technique Multiple Linear Regression was used to calculate the average CO2 emissions in the world in 2050, based on some factors like Human Development Index, Air Quality Index and the percentage of human-made land dominance. The results showed how the climate and environmental changes were evolving over the years. The data helped understand how big the problem is. Overall results were better than expected, it seems that some countries have put a good amount of effort into improving their environmental policies. However, there are still some countries (like China and the USA) that need to work on these policies

Earth Observations in Social Science Research for Management of Natural Resources and the Environment: Identifying the Contribution of the U.S. Land Remote Sensing (Landsat) Program

This paper surveys and describes the peer-reviewed social science literature in which data from the U.S. land remote sensing program, Landsat, inform public policy in managing natural resources and the environment. The Landsat program has provided the longest collection of observations of Earth from the vantage point of space. The paper differentiates two classes of research: methodology exploring how to use the data (for example, designing and testing algorithms or verifying the accuracy of the data) and applications of data to decisionmaking or policy implementation in managing land, air quality, water, and other natural and environmental resources. Selection of the studies uses social science-oriented bibliographic search indices and expands results of previous surveys that target only researchers specializing in remote sensing or photogrammetry. The usefulness of Landsat as a basis for informing public investment in the Landsat program will be underestimated if this body of research goes unrecognized.natural resources policy, environmental policy, Landsat, social science, environmental management

Models in the Cloud: Exploring Next Generation Environmental Software Systems

There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Towards better integration of environmental science in society: lessons from BONUS, the joint Baltic Sea environmental research and development programme

Integration of environmental science in society is impeded by the large gap between science and policy that is characterised by weaknesses in societal relevance and dissemination of science and its practical implementation in policy. We analyse experiences from BONUS, the policy-driven joint Baltic Sea research and development programme (2007–2020), which is part of the European Research Area (ERA) and involves combined research funding by eight EU member states. The ERA process decreased fragmentation of Baltic Sea science and BONUS funding increased the scientific quality and societal relevance of Baltic Sea science and strengthened the science-policy interface. Acknowledging the different drivers for science producers (academic career, need for funding, peer review) and science users (fast results fitting policy windows), and realising that most scientists aim at building conceptual understanding rather than instrumental use, bridges can be built through strategic planning, coordination and integration. This requires strong programme governance stretching far beyond selecting projects for funding, such as coaching, facilitating the sharing of infrastructure and data and iterative networking within and between science producer and user groups in all programme phases. Instruments of critical importance for successful science-society integration were identified as: (1) coordinating a strategic research agenda with strong inputs from science, policy and management, (2) providing platforms where science and policy can meet, (3) requiring cooperation between scientists to decrease fragmentation, increase quality, clarify uncertainties and increase consensus about environmental problems, (4) encouraging and supporting scientists in disseminating their results through audience-tailored channels, and (5) funding not only primary research but also synthesis projects that evaluate the scientific findings and their practical use in society – in close cooperation with science users − to enhance relevance, credibility and legitimacy of environmental science and expand its practical implementation

Vetoes for Inspiral Triggers in LIGO Data

Presented is a summary of studies by the LIGO Scientific Collaboration's Inspiral Analysis Group on the development of possible vetoes to be used in evaluation of data from the first two LIGO science data runs. Numerous environmental monitor signals and interferometer control channels have been analyzed in order to characterize the interferometers' performance. The results of studies on selected data segments are provided in this paper. The vetoes used in the compact binary inspiral analyses of LIGO's S1 and S2 science data runs are presented and discussed.Comment: Submitted to Classical and Quantum Gravity for the GWDAW-8 proceeding

A practitioners guide to managing geoscience information

In the UK the Natural Environment Research Council manages its scientific data holdings through a series of Environmental Data Centres1 Within the Earth Science sector the National Geoscience Data Centre covering Atmosphere, Bioinformatics, Earth Sciences, Earth Observation, Hydrology, Marine Science and Polar Science. 2 - Risk Reduction; (NGDC), a component of the British Geological Survey (BGS), is responsible for managing the geosciences data resource. The purpose of the NGDC is to maintain the national geoscience database and to ensure efficient and effective delivery by providing geoscientists with ready to access data and information that is timely, fit for purpose, and in which the user has confidence. The key benefits that NERC derives from this approach are: - Increased Productivity; and - Higher Quality Science. The paper briefly describes the key benefits of managing geoscientific information effectively and describes how these benefits are realised within the NGDC and BGS