Characterising Volunteers' Task Execution Patterns Across Projects on Multi-Project Citizen Science Platforms

Citizen science projects engage people in activities that are part of a scientific research effort. On multi-project citizen science platforms, scientists can create projects consisting of tasks. Volunteers, in turn, participate in executing the project's tasks. Such type of platforms seeks to connect volunteers and scientists' projects, adding value to both. However, little is known about volunteer's cross-project engagement patterns and the benefits of such patterns for scientists and volunteers. This work proposes a Goal, Question, and Metric (GQM) approach to analyse volunteers' cross-project task execution patterns and employs the Semiotic Inspection Method (SIM) to analyse the communicability of the platform's cross-project features. In doing so, it investigates what are the features of platforms to foster volunteers' cross-project engagement, to what extent multi-project platforms facilitate the attraction of volunteers to perform tasks in new projects, and to what extent multi-project participation increases engagement on the platforms. Results from analyses on real platforms show that volunteers tend to explore multiple projects, but they perform tasks regularly in just a few of them; few projects attract much attention from volunteers; volunteers recruited from other projects on the platform tend to get more engaged than those recruited outside the platform. System inspection shows that platforms still lack personalised and explainable recommendations of projects and tasks. The findings are translated into useful claims about how to design and manage multi-project platforms.Comment: XVIII Brazilian Symposium on Human Factors in Computing Systems (IHC'19), October 21-25, 2019, Vit\'oria, ES, Brazi

Citizen Science Terminology Matters: Exploring Key Terms

Much can be at stake depending on the choice of words used to describe citizen science, because terminology impacts how knowledge is developed. Citizen science is a quickly evolving field that is mobilizing people’s involvement in information development, social action and justice, and large-scale information gathering. Currently, a wide variety of terms and expressions are being used to refer to the concept of ‘citizen science’ and its practitioners. Here, we explore these terms to help provide guidance for the future growth of this field. We do this by reviewing the theoretical, historical, geopolitical, and disciplinary context of citizen science terminology; discussing what citizen science is and reviewing related terms; and providing a collection of potential terms and definitions for ‘citizen science’ and people participating in citizen science projects. This collection of terms was generated primarily from the broad knowledge base and on-the-ground experience of the authors, by recognizing the potential issues associated with various terms. While our examples may not be systematic or exhaustive, they are intended to be suggestive and invitational of future consideration. In our collective experience with citizen science projects, no single term is appropriate for all contexts. In a given citizen science project, we suggest that terms should be chosen carefully and their usage explained; direct communication with participants about how terminology affects them and what they would prefer to be called also should occur. We further recommend that a more systematic study of terminology trends in citizen science be conducted

Synthesizing multiple data sources to understand the population and community ecology of California trees

In this work, I answer timely questions regarding tree growth, tree survival, and community change in California tree species, using a variety of sophisticated statistical and remote sensing tools. In Chapter 1, I address tree growth for a single tree species with a thorough explanation of hierarchical state-space models for forest inventory data. Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation make these data challenging to use for growth estimation. I account for these complexities and incorporate potential limiting factors into a hierarchical state-space model. I estimate the diameter growth of white fir in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth. In Chapter 2, I expand my state-space modeling to examine survival in seven tree species, as well as investigating the results of modeling them in aggregate and comparing with the individual species models. Declining tree survival is a complex, well-recognized problem, but studies have been largely limited to relatively rare old-growth forests or low-diversity systems, and to models which are species-aggregated or cannot easily accommodate yearly climate variables. I estimate survival models for a relatively diverse second-growth forest in the Sierra Nevada of California using a hierarchical state-space framework. I account for a mosaic of measurement intervals and random plot variation, and I directly include yearly stand development variables alongside climate variables and topographic proxies for nutrient limitation. My model captures the expected dependence of survival on tree size. At the community level, stand development variables account for decreasing survival trends, but species-specific models reveal a diversity of factors influencing survival. Species time trends in survival do not always conform to existing theories of Sierran forest dynamics, and size relationships with survival differ for each species. Within species, low survival is concentrated in susceptible subsets of the population and single estimates of annual survival rates do not reflect this heterogeneity in survival. Ultimately only full population dynamics integrating these results with models of recruitment can address the potential for community shifts over time. In Chapter 3, I combine statistical modeling with remote sensing techniques to investigate whether topographic variables influence changes in woody cover. In the North Coast of California, changes in fire management have resulted in conversion of oak woodland into coniferous forest, but the controls on this slow transition are unknown. Historical aerial imagery, in combination with Object-Based Image Analysis (OBIA), allows us to classify land cover types from the 1940s and compare these maps with recent cover. Few studies have used these maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. I study the change in woody cover in California's North Coast using historical and recent high-spatial-resolution imagery. I classify the imagery using eCognition Developer and aggregate the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. I use Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. I find that historical woody cover has a consistent positive effect on current woody cover, and that the spatial term in the model is significant even after controlling for historical cover. Specific topographic variables emerge as important for different sites at different scales, but no overall pattern emerges across sites or scales for any of the topographic variables I tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change. I conclude that these techniques are promising but a framework is needed for sensitivity analysis, as modeling results can depend strongly on variable selection and model structure. (Abstract shortened by UMI.

Où sont passé·e·s les coauteurs·trices ?

Longtemps marginale, la recherche participative est devenue une approche de plus en plus répandue dans les sciences sociales, biophysiques et les études interdisciplinaires. L’augmentation générale du nombre de publications tirées d’une recherche participative a soulevé la question de la reconnaissance des contributions de collaborateurs et collaboratrices non universitaires. Au moyen de méthodes qualitatives et quantitatives, nous avons analysé les tendances et modèles des pratiques d’autorat et de reconnaissance à partir d’un échantillon de 262 articles de revue restituant les résultats de recherches participatives sur les moyens d’existence en milieu rural, publiés entre 1975 et 2013. Seuls 6 % des chercheuses et chercheurs reconnaissent les contributions intellectuelles de leurs collaborateurs·trices non universitaires en leur attribuant un statut de coauteur·trice, tandis que 51 % se contentent de remerciements. En nous appuyant sur les entretiens menés avec les auteurs·trices principaux des articles coécrits, nous avons examiné les facteurs expliquant les cas où la qualité d’auteur était partagée avec les collaborateurs·trices non universitaires. Malgré un certain nombre d’obstacles, les chercheuses et chercheurs ayant opté pour le coautorat justifient ce choix par un souci d’éthique scientifique, la volonté de reconnaître toutes les contributions intellectuelles et un effort de décolonisation épistémique. Notre propos est non seulement de montrer que la cosignature peut être un vecteur important de justice épistémique dans la recherche participative, mais aussi d’encourager ses praticien·ne·s à faire des discussions sur les enjeux d’autorat avec leurs collaborateurs·trices une partie intégrante de la recherche-action participative [engaged scholarship]. Nous soulignons également que les contributions non universitaires au savoir scientifique doivent être prises en considération dans la compréhension des pratiques de recherche.Originally marginal, participatory research has become an increasingly important methodology in the social, biophysical, and interdisciplinary sciences. The overall increase in publications based on participatory research has raised questions about crediting the contributions of nonacademic collaborators. Using qualitative and quantitative methods, we analyzed trends and patterns in authorship and acknowledgment practices in a sample of 262 journal articles reporting on participatory research on rural livelihoods published from 1975 to 2013. Six percent of the researchers recognized the intellectual contributions of their nonacademic collaborators with coauthorship and 51 percent with acknowledgment. Through interviews with lead authors of coauthored articles, we analyzed factors that shaped whether authorship was shared with nonacademic collaborators. Despite facing numerous barriers, researchers were motivated to coauthor in order to recognize intellectual contributions, practice research ethics, and work toward epistemic decolonization. We argue that coauthorship can be an important component of epistemic justice in participatory research and encourage participatory researchers to discuss authorship with their nonacademic collaborators as a routine component of engaged scholarship. We also note that nonacademics’ contributions to scientific knowledge need to be taken into account in understandings of the practice of science.La investigación participativa, inicialmente al margen de otras investigaciones, ha progresado hasta llegar a ser una metodología destacada en las ciencias sociales, biofísicas e interdisciplinarias. En general, el aumento de las publicaciones de investigación participativa plantea interrogantes acerca de las contribuciones hechas por colaboradores no académicos y la acreditación de tales. Se analizaron pautas y patrones sobre autoría y prácticas de reconocimiento, empleándose métodos cualitativos y cuantitativos, se revisaron 262 publicaciones bajo el rubro de investigación participativa enfocada en los medios de subsistencia rural, divulgadas entre de 1975 y 2013. El seis por ciento de los investigadores agradecen las contribuciones intelectuales de sus colaboradores no académicos por medio de coautoría y un 51 por ciento por medio de reconocimiento. A través de entrevistas con autores de artículos coescritos, se analizaron los factores influyentes para que la autoría resultara compartida –o no –con los cooperantes no académicos. A pesar de los múltiples obstáculos, los investigadores compartieron el crédito de autoría con alacridad para reconocer las contribuciones intelectuales de sus colaboradores, para ejercer éticas investigativas y, para encauzar una descolonización epistémica. Se propone que la coautoría constituiría un notable factor en la justicia epistémica dentro de la investigación participativa e igualmente alentaría a los investigadores a dialogar con sus colaboradores no académicos sobre autoría empujando a la adaptación este dialogo como un factor rutinario en la erudición comprometida. Asimismo, se señala la necesidad de considerar las contribuciones no académicas al conocimiento científico y al entendimiento de su practicidad

Appendix F. Latent unmeasured sizes for nine trees.

Latent unmeasured sizes for nine trees

Supplement 1. BUGS model code for full model.

<h2>File List</h2><div> <p><a href="WhiteFirBUGSModel.txt">WhiteFirBUGSModel.txt</a> (MD5: f7c238534f45c8cb363482a385d7f078) </p> </div><h2>Description</h2><div> <p>This Supplement includes the BUGS specification of the model. When using OpenBUGS or WinBUGS, open this file, go to the menu “Model>Specification” and when the Specification Tool appears, click “Check Model.” If the code works, the program displays in the status bar at the bottom of the window “Model is syntactically correct.” </p> </div

Appendix D. Details on model estimation and the evaluation of model results.

Details on model estimation and the evaluation of model results

Appendix B. Measurement methods and auditing of explanatory variables (tree size, basal area, insolation, elevation, slope, annual water deficit, and soil type).

Measurement methods and auditing of explanatory variables (tree size, basal area, insolation, elevation, slope, annual water deficit, and soil type)

Appendix E. Table of parameter estimates.

Table of parameter estimates

Appendix G. Examples of simpler models.

Examples of simpler models