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Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects.

In a pooled analysis of nine clinical trials involving almost 5000 women with breast cancer who underwent examination of the bone marrow for metastatic cancer cells, the presence of metastases in the bone marrow at the time of diagnosis of breast cancer was associated with a poor prognosis. In trials involving almost 5000 women with breast cancer, the presence of micrometastases in the bone marrow at the time of diagnosis of breast cancer was associated with a poor prognosis. Data from experiments in animals 1 performed in the 1960s and from more recent immunocytochemical 2 , 3 and molecular 4 , 5 studies suggest that lymph-node involvement does not accurately predict hematogenous dissemination of cancer cells, nor is hematogenous dissemination necessarily associated with lymph-node involvement. 6 , 7 During the past two decades, several studies have assessed the prevalence and prognostic value of hematogenous dissemination of tumor cells in patients with node-positive and node-negative breast cancer. 3 , 8 – 15 The influence of the presence of micrometastasis in the bone marrow on prognosis has been shown in patients with identical stages of breast cancer, as defined by tumor . . .

We investigated the motivations of participants in two environment-focused citizen science projects using an online questionnaire. The questions focused on the reasons for initial engagement and in how far these motivations were fulfilled by participating. The two projects, CrowdWater and Naturkalender (English: Nature’s Calendar), use similar smartphone applications to collect data on water and phenology, respectively. The answers to the individual statements were analyzed based on a categorization framework that was previously used with other citizen science projects. The motivations to participate in the projects were similar for the two projects but there were also some differences. They were altruistic and related to participants’ principles (e.g., to uphold a moral principle, such as through conservation). The main motivations for becoming engaged in the projects were to contribute to science, due to an interest in the project topic, and to protect nature. More CrowdWater respondents were motivated by being asked to participate than Naturkalender respondents. Naturkalender participants and participants in the 50–59-year age group of both projects agreed most to enjoying their participation, being outside and active, and learning something new. More super-users, i.e., users who participated at least once per week, were interested in sharing their knowledge and experience with others than occasional participants. This was particularly true for super-users in Naturkalender. Based on the results of this study, we recommend that to help sustain involvement of the most active participants, projects should focus on recruiting participants who are already interested in the topic, and highlighting opportunities to share knowledge, be outdoors, and contribute to science.

Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

Some form of training is often necessary for citizen science projects. While in some citizen science projects, it is possible to keep tasks simple so that training requirements are minimal, other projects include more challenging tasks and, thus, require more extensive training. Training can be a hurdle to joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated whether an online game that was originally developed for data quality control in a citizen science project can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to place the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however, this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project-related tasks intuitively by observing actual gauge placements by other citizen scientists in the game and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or the performance increase through the training. These results demonstrate the value of an online game for training. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods, particularly when other information materials are not used extensively by citizen scientists.

Japan has been collecting data on the composition of its waste for years and is in the fortunate position to make accurate predictions of future developments. It can trace the changes of the past and establish why certain shifts in material use have happened.

The extent of axillary lymph node involvement represents the foremost important prognostic parameter in primary breast cancer, and, thus, is one of the main determinants for subsequent systemic treatment. Nevertheless, the relevance of the initial axillary lymph node status on survival after disease recurrence is discussed controversially. Persisting prognostic impact after relapse would identify lymph node status as a marker for tumor biology, in contrast to a simply time-dependent phenomenon. Retrospective analysis of 813 patients with locoregional or distant recurrence of primary breast cancer, who were primarily diagnosed with their disease at the I. Frauenklinik, Ludwig-Maximilians-University, Munich, and the University Hospital in Berlin-Charlottenburg, Germany, between 1963 and 2000. To be eligible, patients were required to have been treated for resectable breast cancer free of distant disease at the time of primary diagnosis, and must have undergone systematic axillary lymph node dissection. Patients with unknown tumor size or nodal status were excluded from the study. All data were gathered contemporaneously and compared with original patients files, as well as the local cancer registry, ensuring high quality of data. The median observation time was 60 (standard deviation 44) months. At time of primary diagnosis, 273 patients (33.6%) were node-negative, while axillary lymph node metastases were detected in 540 patients (66.4%). In univariate analysis tumor size, axillary lymph node status, histopathological grading, hormone receptor status, as well as peritumoral lymphangiosis and haemangiosis carcinomatosa were significantly correlated with survival after relapse (all, P < 0.0001). Kaplan-Meier analysis estimated the median survival time after relapse in node-negative patients to be 42 months (31-52 months, 95% CI), and 20 months in patients with 1-3 axillary lymph node metastases (16-24 months, 95% CI), compared to 13 months in patients with at least 4 involved axillary nodes (12-15 months, 95% CI). Multivariate logistic regression analysis, allowing for tumor size, axillary lymph node status, histopathological grading, presence of lymphangiosis carcinomatosa, relapse site and disease-free interval confirmed all parameters, except of histopathological grading (P = 0.14), as significant, independent risk factors for cancer associated death. Subgroup analyses, accounting for site of relapse and duration of disease-free interval, confirmed primary lymph node status as independent predictor for cancer-associated death after relapse. Lymph node involvement at primary diagnosis of breast cancer patients predicts an unfavorable outcome after first recurrence, independently of the site of relapse and disease-free interval. These observations support the hypothesis that primary lymph node involvement is not a merely time-dependent indicator for tumor progression, but indicates tumors with aggressive biological behavior.

The number of axillary lymph-node metastases is not only a function of disease progression in primary breast cancer, but is also influenced by the intra-mammary location of the tumor. Nevertheless, the prognostic role of the tumor site is discussed controversially. The objective of this study was to analyze the impact of primary-tumor location on axillary lymph-node involvement, relapse, and mortality risk by univariate and multivariate analysis, in patients both with and without systemic and loco-regional treatment. Retrospective analysis was conducted on 2,414 patients at the I. Frauenklinik, Ludwig-Maximilians University, Munich and Berlin-Charlottenburg, who underwent R(0) resection of the primary tumor and systematic axillary lymph-node dissection (at least five lymph nodes resected) for UICC I-III-stage breast cancer. Patients with unknown tumor site, multifocal tumor spread, central tumor location, or tumor location within 15 degrees of the border between outer and inner quadrants were excluded from the study. Median observation time was 6.7 years. The primary tumor site was within or between the medial quadrants of the breast in 33.6% of the patients ( n=810) and in the lateral hemisphere of the breast in 66.4% ( n=1,604). Tumor size, histopathological grading, and estrogen receptor status were balanced between patients with lateral and medial tumor location. Metastatic axillary lymph-node involvement was significantly associated with a lateral tumor location ( P<0.0001). The mean number of axillary lymph-node metastases was increased by 29% in cases with lateral tumor location (2.2 vs 1.7, P=0.003). In a multivariate logistic regression analysis allowing for tumor location, estrogen receptor status, grading and tumor size, tumor location was confirmed as a significant risk factor ( P=0.02) for axillary lymph-node involvement. Tumor location, however, did not correlate with either disease-free survival (DFS) or overall survival (OS), by univariate (DFS: P=0.41; OS: P=0.57) or by multivariate analysis (DFS: P=0.16; OS: P=0.98). We conclude that there is no sufficient evidence to support any independent prognostic significance of intra-mammary tumor location in early breast cancer. However, medial tumor location may lead to the underestimation of axillary lymph-node involvement.

Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.