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The objective of this study was to describe the purpose, methods, and effects of the Pac-12 Health Analytics Program (HAP) approach on sports medicine informatics, research, analytics, and health care operations. Sports injury-surveillance initiatives have been supporting the clinical research community in sports medicine for nearly 4 decades. Whereas the initial systems tracked only a few sports, current surveillance programs have expanded to include entire professional and elite athlete organizations, providing important statistics on sports injury risk management. The HAP is a conference-wide data-sharing and-analytics program. It collects authorized, deidentified clinical data, encompassing multiple domains of sports medicine injury management, including sports injuries and illnesses, concussions, risk exposure, and COVID-19 testing elements. The HAP provides clinicians with access to curated data to inform evidence-based practice and support local health care operations with respect to emerging sports injury trends. The HAP supplies approved research groups with access to a data repository that describes a homogeneous, elite intercollegiate athlete sample, thereby supporting nonresearch clinical initiatives as well as contributions to peer-reviewed research that can improve the health and well-being of Pac-12 student-athletes. The HAP is a novel approach to sports injury epidemiology and surveillance that has allowed the Pac-12 Conference to meet larger objectives regarding improving the student-athlete experience and clinical research among its member schools. Data quality control has improved the accuracy of the data and value to clinical athletic trainers within the conference. Curated dashboards displaying aggregated project data offer clinicians data-driven decision-making tools that help inform sports injury risk management. As of 2021, the HAP had supported more than 3 dozen data requests. These investigations have resulted in numerous peer-reviewed research contributions to the sports medicine community with findings that have great potential to improve the health and well-being of Pac-12 student-athletes. [ABSTRACT FROM AUTHOR]

Due to the fact that existing database systems are increasingly more difficult to use, improving the quality and the usability of database systems has gained tremendous momentum over the last few years. In particular, the feature of explaining why some expected tuples are missing in the result of a query has received more attention. In this paper, we study the problem of explaining missing answers to top-k queries in the context of SQL (i.e., with selection, projection, join, and aggregation). To approach this problem, we use the query-refinement method. That is, given as inputs the original top-k SQL query and a set of missing tuples, our algorithms return to the user a refined query that includes both the missing tuples and the original query results. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently. [ABSTRACT FROM AUTHOR]

A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification. [ABSTRACT FROM AUTHOR]

Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organization, or scales, a network may encompass and are therefore limited. In this paper, we present a method compatible with global and local criteria that enables fast multi-scale community detection on large networks. The method is derived in two algorithms, one for each type of criterion, and implemented with six known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore, this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches. In particular, our results suggest that global criteria seem to be more robust to noise and thus more accurate than local criteria. [ABSTRACT FROM PUBLISHER]

There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future. [ABSTRACT FROM AUTHOR]

When identifying conservation priorities, the accuracy of conservation assessments is constrained by the quality of data available. Despite previous efforts exploring how to deal with imperfect datasets, little is known about how data uncertainty translates into errors in conservation planning outcomes. Here, we evaluate the magnitude of commission and omission error, effectiveness and efficiency of conservation planning outcomes derived from three datasets with increasing data quality. We demonstrate that investing in data acquisition might not always be the best strategy as the magnitude of errors introduced by new sites/species can exceed the benefits gained. There was a trade-off between effectiveness and efficiency due to poorly sampled rare species. Given that data acquisition is limited by the high cost and time required, we recommend focusing on improving the quality of data for those species with the highest level of uncertainty (rare species) when acquiring new data. [ABSTRACT FROM AUTHOR]

The author discusses the importance of data quality in a spatial component. He relates that it has become an issue due to the growing use of terrestrial, airborne, and spaceborne sensors, and systems based on locations. He adds that the new infrastructures for spatial data computing and management have been developed through innovations in computer science, like parallel computing, distributed computing, and multidimensional and dynamic database management systems. Information on the content of the journal are also included.

Increasingly powerful computers and increased emphasis on evidence based decision making are creating a demand for merging and integrating data from different sources into a single data set. The demand for data is outstripping our ability to ensure data integrity, and sometimes analysis is performed on data that are not appropriate for the purposes they are used for. Here we describe problems that arise when data from different sources are merged, and we suggest that one way to add context to data so that users can make informed decisions about their ontological context is through ontology-based metadata. Examples of the problem are taken from health data with emphasis on difficulties in standardizing Emergency Room wait times. We describe eight fields that can be used to capture contextual metadata. These fields are captured using ethnographic methods from users and database stewards who frequently understand precisely how context and institutional usage have shaped interpretation of semantic fields. We argue that attaching a portable archive of ontological context to travel with data—based on information from users and developers—is a means of ensuring that data are integrated and compared in multiple contexts with greater integrity and more robust results. [ABSTRACT FROM AUTHOR]

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called "pose estimation" algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires "training" images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab. [ABSTRACT FROM AUTHOR]

Background: Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path. Results: We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field. Conclusions: We leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate). [ABSTRACT FROM AUTHOR]