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Aims: The International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter‐)national pathology and cancer organisations, is an initiative aimed at providing a unified international approach to reporting cancer. ICCR recently published new data sets for the reporting of invasive breast carcinoma, surgically removed lymph nodes for breast tumours and ductal carcinoma in situ, variants of lobular carcinoma in situ and low‐grade lesions. The data set in this paper addresses the neoadjuvant setting. The aim is to promote high‐quality, standardised reporting of tumour response and residual disease after neoadjuvant treatment that can be used for subsequent management decisions for each patient. Methods: The ICCR convened expert panels of breast pathologists with a representative surgeon and oncologist to critically review and discuss current evidence. Feedback from the international public consultation was critical in the development of this data set. Results: The expert panel concluded that a dedicated data set was required for reporting of breast specimens post‐neoadjuvant therapy with inclusion of data elements specific to the neoadjuvant setting as core or non‐core elements. This data set proposes a practical approach for handling and reporting breast resection specimens following neoadjuvant therapy. The comments for each data element clarify terminology, discuss available evidence and highlight areas with limited evidence that need further study. This data set overlaps with, and should be used in conjunction with, the data sets for the reporting of invasive breast carcinoma and surgically removed lymph nodes from patients with breast tumours, as appropriate. Key issues specific to the neoadjuvant setting are included in this paper. The entire data set is freely available on the ICCR website. Conclusions: High‐quality, standardised reporting of tumour response and residual disease after neoadjuvant treatment are critical for subsequent management decisions for each patient. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patient’s lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. OBJECTIVE: We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. RESULTS: In this study, we use the Resegmentation Method for various popular network architectures. CONCLUSION: By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model. [ABSTRACT FROM AUTHOR]

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics. Plain Language Summary: This work introduces a new particle microphysics data set that is useful for improving weather prediction models and in enhancing precipitation estimation techniques. The data set, produced from National Aeronautics and Space Administration's precipitation imaging package, is comprehensive, well documented, and easy to access. It includes observations from 10 locations across the Northern Hemisphere over 10 years, providing information on both rain and snow. This information includes details like particle size, speed, and density, as well as estimates of rainfall and snowfall rates. The data has been standardized and checked for quality, making it reliable and easy to use. This product is a valuable resource for refining methods to measure precipitation, and offers new insights into regional and seasonal precipitation patterns. Key Points: This data set contains high temporal resolution, disdrometer‐derived precipitation microphysics observations from 10 sites over 10 yearsRigorous quality control practices yield a scalable, self‐describing data set packaged into a common, standardized NetCDF formatThe data's diverse geographic and environmental coverage offers new insights into regional and seasonal precipitation processes and patterns [ABSTRACT FROM AUTHOR]

Aims: Mesothelioma is a rare malignancy of the serosal membranes that is commonly related to exposure to asbestos. Despite extensive research and clinical trials, prognosis to date remains poor. Consistent, comprehensive and reproducible pathology reporting form the basis of all future interventions for an individual patient, but also ensures that meaningful data are collected to identify predictive and prognostic markers. Methods and results: This article details the International Collaboration on Cancer Reporting (ICCR) process and the development of the international consensus mesothelioma reporting data set. It describes the 'core' and 'non‐core' elements to be included in pathology reports for mesothelioma of all sites, inclusive of clinical, macroscopic, microscopic and ancillary testing considerations. An international expert panel consisting of pathologists and a medical oncologist produced a set of data items for biopsy and resection specimens based on a critical review and discussion of current evidence, and in light of the changes in the 2021 WHO Classification of Tumours. The commentary focuses particularly upon new entities such as mesothelioma in situ and provides background on relevant and essential ancillary testing as well as implementation of the new requirement for tumour grading. Conclusion: We recommend widespread and consistent implementation of this data set, which will facilitate accurate reporting and enhance the consistency of data collection, improve the comparison of epidemiological data, support retrospective research and ultimately help to improve clinical outcomes. To this end, all data sets are freely available worldwide on the ICCR website (www.iccr‐cancer.org/data‐sets). [ABSTRACT FROM AUTHOR]

The article considers one of the main problems of data analysis, i.e., estimation of parameters characterizing the data sample of a constant quantity. Data analysis is required in all areas of experimental physics to obtain reliable measurement results. To describe sample units under bilateral constraints on their error, the apparatus of interval analysis and statistics is used. In particular, data homogeneity in the sample is described using various consistency measures. A set of three consistency measures is presented that describe different relationships between sample units. On the basis of the considered set, a combined sample consistency measure is proposed that can simultaneously provide outer and inner estimates of the quantity under study. The specified estimates are important in solving a massive data processing problem (i.e., processing of a set of samples obtained under different measurement conditions). The article provides necessary information on interval analysis and various interval arithmetics. The relationships between the proposed combined measure and the results of computations with interval twins and fuzzy sets are considered. This combined measure can be used in solving the massive data processing problem typically addressed in theoretical and applied semiconductor physics. A practical example is presented of using the combined sample consistency measure in the testing of solar transducers against a reference transducer as part of the study of their spectral properties and quantum yield. [ABSTRACT FROM AUTHOR]

This article describes a new database (named "EMAP") of 145 individuals' reactions to emotion‐provoking film clips. It includes electroencephalographic and peripheral physiological data as well as moment‐by‐moment ratings for emotional arousal in addition to overall and categorical ratings. The resulting variation in continuous ratings reflects inter‐individual variability in emotional responding. To make use of the moment‐by‐moment data for ratings as well as neurophysiological activity, we used a machine learning approach. The results show that algorithms that are based on temporal information improve predictions compared to algorithms without a temporal component, both within and across participant modeling. Although predicting moment‐by‐moment changes in emotional experiences by analyzing neurophysiological activity was more difficult than using aggregated experience ratings, selecting a subset of predictors improved the prediction. This also showed that not only single features, for example, skin conductance, but a range of neurophysiological parameters explain variation in subjective fluctuations of subjective experience. To increase our understanding of emotion, realistic, constantly changing reports of experience are needed to investigate relationships between subjective ratings and physiological reactions. A new data set to explore moment‐by‐moment personal experiences of emotion in conjunction with neurophysiological responses is presented. Initial modeling results show that although it is difficult to predict moment‐by‐moment ratings, predictions are improved by algorithms that take temporal information into account. [ABSTRACT FROM AUTHOR]

The 2021 Amazon Last Mile Routing Research Challenge, hosted by Amazon.com's Last Mile Research team, and scientifically supported by the Massachusetts Institute of Technology's Center for Transportation and Logistics, prompted participants to leverage real operational data to find new and better ways to solve a real-world routing problem. In this article, we describe the data set released for the research challenge, which includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world data set excludes any personally identifiable information: all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity. Although multiple synthetic benchmark data sets are available in the literature, the data set of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available data set to include instances based on real-world operational routing data. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. [ABSTRACT FROM AUTHOR]

Estimating depth from images is a common technique in 3D perception. However, dealing with non-Lambertian materials, e.g., transparent or specular, is still nowadays an open challenge. However, to overcome this challenge with deep stereo matching networks or monocular depth estimation, data sets with non-Lambertian objects are mandatory. Currently, only few real-world data sets are available. This is due to the high effort and time-consuming process of generating these data sets with ground truth. Currently, transparent objects must be prepared, e.g., painted or powdered, or an opaque twin of the non-Lambertian object is needed. This makes data acquisition very time consuming and elaborate. We present a new measurement principle for how to generate a real data set of transparent and specular surfaces without object preparation techniques, which greatly reduces the effort and time required for data collection. For this purpose, we use a thermal 3D sensor as a reference system, which allows the 3D detection of transparent and reflective surfaces without object preparation. In addition, we publish the first-ever real stereo data set, called TranSpec3D, where ground truth disparities without object preparation were generated using this measurement principle. The data set contains 110 objects and consists of 148 scenes, each taken in different lighting environments, which increases the size of the data set and creates different reflections on the surface. We also show the advantages and disadvantages of our measurement principle and data set compared to the Booster data set (generated with object preparation), as well as the current limitations of our novel method. [ABSTRACT FROM AUTHOR]

In view of the problems of traditional Chinese license plate recognition methods, such as the requirement of scenes, poor real-time performance, and inability to deploy on edge devices, this study proposes a Chinese license plate detection and recognition method based on YOLO(You Only Look Once) in unconstrained scenes. This method is divided into two modules: license plate detection and license plate character recognition. In the license plate detection part, the improved YOLOv5 model is used to predict four groups of key points for license plate correction based on the prediction of target candidate regions, and the pre-training model trained on the COCO data set is used for training, which reduces the error detection problem caused by the complex environment and has high real-time performance. In the license plate character recognition part, the CRNN(Convolutional Recurrent Neural Network) model is improved, which greatly reduces the parameters and computation of the algorithm, so that it can be successfully deployed in various edge devices. Experimental results show that the proposed method can efficiently detect and recognize license plates in complex environments. The map value of the proposed license plate detection model is 3.0% higher than that of Retina-face in the license plate detection data set. Compared with LPR-Net, the accuracy of license plate character recognition model in license plate recognition data set is improved by 4.2%. [ABSTRACT FROM AUTHOR]

[Significance] The scientific dataset of agricultural pests and diseases is the foundation for monitoring and warning of agricultural pests and diseases. It is of great significance for the development of agricultural pest control, and is an important component of developing smart agriculture. The quality of the dataset affecting the effectiveness of image recognition algorithms, with the discovery of the importance of deep learning technology in intelligent monitoring of agricultural pests and diseases. The construction of high-quality agricultural pest and disease datasets is gradually attracting attention from scholars in this field. In the task of image recognition, on one hand, the recognition effect depends on the improvement strategy of the algorithm, and on the other hand, it depends on the quality of the dataset. The same recognition algorithm learns different features in different quality datasets, so its recognition performance also varies. In order to propose a dataset evaluation index to measure the quality of agricultural pest and disease datasets, this article analyzes the existing datasets and takes the challenges faced in constructing agricultural pest and disease image datasets as the starting point to review the construction of agricultural pest and disease datasets. [Progress] Firstly, disease and pest datasets are divided into two categories: private datasets and public datasets. Private datasets have the characteristics of high annotation quality, high image quality, and a large number of inter class samples that are not publicly available. Public datasets have the characteristics of multiple types, low image quality, and poor annotation quality. Secondly, the problems faced in the construction process of datasets are summarized, including imbalanced categories at the dataset level, difficulty in feature extraction at the dataset sample level, and difficulty in measuring the dataset size at the usage level. These include imbalanced inter class and intra class samples, selection bias, multi-scale targets, dense targets, uneven data distribution, uneven image quality, insufficient dataset size, and dataset availability. The main reasons for the problem are analyzed by two key aspects of image acquisition and annotation methods in dataset construction, and the improvement strategies and suggestions for the algorithm to address the above is‐ sues are summarized. The collection devices of the dataset can be divided into handheld devices, drone platforms, and fixed collection devices. The collection method of handheld devices is flexible and convenient, but it is inefficient and requires high photography skills. The drone platform acquisition method is suitable for data collection in contiguous areas, but the detailed features captured are not clear enough. The fixed device acquisition method has higher efficiency, but the shooting scene is often relatively fixed. The annotation of image data is divided into rectangular annotation and polygonal annotation. In image recognition and detection, rectangular annotation is generally used more frequently. It is difficult to label images that are difficult to separate the target and background. Improper annotation can lead to the introduction of more noise or incomplete algorithm feature extraction. In response to the problems in the above three aspects, the evaluation methods are summarized for data distribution consistency, dataset size, and image annotation quality at the end of the article. [Conclusions and Prospects] The future research and development suggestions for constructing high-quality agricultural pest and disease image datasets based are proposed on the actual needs of agricultural pest and disease image recognition:(1) Construct agricultural pest and disease datasets combined with practical usage scenarios. In order to enable the algorithm to extract richer target features, image data can be collected from multiple perspectives and environments to construct a dataset. According to actual needs, data categories can be scientifically and reasonably divided from the perspective of algorithm feature extraction, avoiding unreasonable inter class and intra class distances, and thus constructing a dataset that meets task requirements for classification and balanced feature distribution. (2) Balancing the relationship between datasets and algorithms. When improving algorithms, consider the more sufficient distribution of categories and features in the dataset, as well as the size of the dataset that matches the model, to improve algorithm ac‐ curacy, robustness, and practicality. It ensures that comparative experiments are conducted on algorithm improvement under the same evaluation standard dataset, and improved the pest and disease image recognition algorithm. Research the correlation between the scale of agricultural pest and disease image data and algorithm performance, study the relationship between data annotation methods and algorithms that are difficult to annotate pest and disease images, integrate recognition algorithms for fuzzy, dense, occluded tar‐ gets, and propose evaluation indicators for agricultural pest and disease datasets. (3) Enhancing the use value of datasets. Datasets can not only be used for research on image recognition, but also for research on other business needs. The identification, collection, and annotation of target images is a challenging task in the construction process of pest and disease datasets. In the process of collecting image data, in addition to collecting images, attention can be paid to the collection of surrounding environmental information and host information. This method is used to construct a multimodal agricultural pest and disease dataset, fully leveraging the value of the dataset. In order to focus researchers on business innovation research, it is necessary to innovate the organizational form of data collection, develop a big data platform for agricultural diseases and pests, explore the correlation between multimodal data, improve the accessibility and convenience of data, and provide efficient services for application implementation and business innovation. [ABSTRACT FROM AUTHOR]

Increasing research has been conducted on individuals presenting with self‐harm at emergency departments (EDs). However, less is known about individuals presenting to EDs with only self‐harm ideation. We aimed to describe the characteristics of those attending Irish hospitals with self‐harm ideation and investigate any differences in comparison to those presenting with suicide ideation. A prospective cohort study was conducted on Irish ED presentations due to suicidal and self‐harm ideation. Data were obtained from the service improvement data set of a dedicated nurse‐led National Clinical programme for the assessment of those presenting to Irish emergency departments due to Self‐harm and Suicide‐related Ideation (NCPSHI). A total of 10 602 anonymized presentation data were analysed from 1 January 2018 to 31 December 2019. Descriptive analysis was conducted to compare those with suicidal and self‐harm ideation on sociodemographic and care interventions. Being female and aged <29 were more prevalent among the self‐harm ideation presentations. Compared to the self‐harm ideation group, a higher proportion of those with suicidal thoughts received an emergency care plan (63% vs 58%, p = 0.002) and General Practitioner letter sent within 24 h of presentation (75% vs 69%, p = 0.045). Little variation was found between hospitals for self‐harm ideation in both years. Our study suggests that females and younger populations are more prevalent in hospital presentations due self‐harm ideation, while presentations related to suicidal ideation are more often made by males and involving substance use. Attention should be given to the relationship between clinicians' attitudes towards care provision and the content of suicide‐related ideation ED disclosure. [ABSTRACT FROM AUTHOR]

When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensional matrix to a one-dimensional vector matrix using the Vector Quantization (VQ) algorithm. The data is divided into training and testing. Then it is fed into 23 layers of deep neural networks (DNN) that classify satellite images. The result is 2,145,020 parameters, and the evaluation of performance measures was accuracy = 100%, loopback = 100%, and the result F1 = 100 %. [ABSTRACT FROM AUTHOR]

Sentiment analysis and opinion mining are essential tasks with many prominent application areas, e.g., when researching popular opinions on products or brands. Sentiments expressed in social media can be used in brand name monitoring and indicating fake news. In our survey of previous work, we note that there is no large-scale social media data set with sentiment polarity annotations for Finnish. This publication aims to remedy this shortcoming by introducing a 27,000-sentence data set annotated independently with sentiment polarity by three native annotators. We had three annotators annotate the whole data set, which provides a unique opportunity for further studies of annotator behavior over the sample annotation order. We analyze their inter-annotator agreement and provide two baselines to validate the usefulness of the data set. [ABSTRACT FROM AUTHOR]

Objective: Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). Methods: In this study, using a multicenter resting state functional magnetic resonance imaging (rs‐fMRI) data set, we constructed whole‐brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting‐state, whole‐brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration‐segregation axis," by combining whole‐brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)–based dimensionality reduction. Results: Compared to UTLE, BiTLE had decreased global efficiency (p =.031), and decreased whole brain average participation coefficient across a range of network densities (p =.019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p =.020). Differences in network properties separate BiTLE and UTLE along the integration‐segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration‐segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. Significance: Increased interictal whole‐brain network segregation, as measured by rs‐fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non‐invasively identifying this patient population prior to intracranial electroencephalography or device implantation. [ABSTRACT FROM AUTHOR]

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML (Computer Science / Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study. [ABSTRACT FROM AUTHOR]

The thermodynamics of protein–nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein–Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online. [ABSTRACT FROM AUTHOR]

Distinguishing cause and effect from purely observational data is a fundamental problem in science. Even the atomic bivariate case, seemingly the simplest, is challenging and requires assumptions about the underlying data generating process to identify the direction of cause and effect. In recent years, a variety of approaches have been developed to address this problem, each with its own assumptions, strengths, and weaknesses. In machine learning, common benchmarks with real and synthetic data have been a main driver of innovation. For cause-effect identification, real data as well as synthetic benchmarks have been seminal to inspire the development of new causal methods. In contrast to real-world data, synthetic data can explicitly model data characteristics such as the underlying functional relations and underlying data distributions to assess in detail how methods perform. Currently, a systematic assessment of the state-of-the-art of methods on the latest set of real-world data and comprehensive synthetic data is missing. We provide a detailed and systematic comparison of a range of methods on current real-world data and a novel collection of data sets that systematically models individual data challenges. Our evaluation also covers more recent methods missing in previous studies. The aim is to assist users in finding the most suitable methods for their problem setting and for method developers to identify weaknesses of current methods to improve them or develop new methods. The novel suite of data sets will be contributed to the causeme.net benchmark platform to provide a continuously updated and searchable causal discovery method intercomparison database. [ABSTRACT FROM AUTHOR]

Background: The recent literature on intrahepatic cholestasis of pregnancy raises questions on the best management of such a disease. Improved evidence might be achieved by meta-analyses. Aim: Providing data for allowing individual patients meta-analyses and aggregate data meta-analyses. Methods: Data were collected retrospectively at the Fabia Mater Hospital of Rome (Italy), between 2013 and 2018. Several variables were collected and analyzed according to low-level bile acid (less than 40 lmol/L) and high-level bile acid (at least 40 lmol/L). Eighty-three cases of pregnancy cholestasis, diagnosed according to itching symptoms and excluding bile diseases, were collected and analyzed, both descriptively and inferentially. Conclusion: The analyzed data do not provide significant evidence supporting the use of ursodeoxycholic acid to prevent composite adverse fetal outcomes but they can be included in further meta-analyses. [ABSTRACT FROM AUTHOR]

The absence of young adults in elected legislative assemblies is a democratic deficit with potentially severe repercussions. Yet, research is rarely able to address the issue of age group representation systematically because we are lacking empirical information on the age distribution in parliaments. The Worldwide Age Representation in Parliaments (WARP) data set remedies this dearth of data. It provides information about the numerical presence of age groups in parliaments, spanning across the globe and over time and includes age data on legislators, such as the share of members of parliament (MPs) aged 30 years or under, 35 years or under, or 40 years or under. The data set also reports measures that compare the presence of legislators aged 30 years or under, aged 35 years or under, aged 40 years or under, aged 41 to 60 years, as well as aged 61 years or over in relation to the same age group in the general population of a given country. Moreover, it includes gendered figures, such as the presence of young female MPs. The WARP data set contains data for more than 700 elections in 149 countries, so far, and is freely available online. It allows for a novel analysis of the age composition of legislatures. [ABSTRACT FROM AUTHOR]

Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable. [ABSTRACT FROM AUTHOR]

In view of the problems of low efficiency of manual garbage sorting, heavy tasks and harsh environment, this study proposes a YOLO-based target detection method to realize garbage detection and classification. The model is adjusted through making a specific dataset, using K-means clustering algorithm and Mish activation function. According to the characteristics of the convolutional neural network, the CBAM attention module is embedded in front of each detection head of the YOLO model, combined with PANet to enhance the feature integration ability to improve the accuracy of small target detection. The experimental results show that the garbage detection and classification method proposed in this study can accurately and quickly identify garbage. Compared with YOLOv4, the map value of the proposed model on the garbage data set has increased by 2.81%. The recognition accuracy of Cans can reach 94.56%, and the accuracy of PlasticBottle has increased by 6.36%. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, its automatic learning characteristics and accurate prediction ability make it successful in ground-based cloud classification. More complex and better deep learning networks are applied and studied in the field of ground-based cloud classification. In the past two years, some large-scale ground-based cloud classification data sets have been published, yet there is no literature on fully introduction and use of these large data sets. Here, we list the lately issued massive data sets, then introduce the cloud classification technology, especially detail the latest research progress in deep learning-based cloud classification, and finally assess and compare several classic convolution neural network learning models on their ground-based cloud classification performance. The convolution neural network is verified to be effective in the field of ground-based cloud classification. [ABSTRACT FROM AUTHOR]

Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks. [ABSTRACT FROM AUTHOR]

Objective: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question.Data Sources: A systematic search was performed in Pubmed, ArXiv, MedRxiv, and BioRxiv.Study Selection: We selected all studies that reported on publicly available adult patient-level intensive care datasets.Data Extraction: A total of four publicly available, adult, critical care, patient-level databases were included (Amsterdam University Medical Center data base [AmsterdamUMCdb], eICU Collaborative Research Database eICU CRD], High time-resolution intensive care unit dataset [HiRID], and Medical Information Mart for Intensive Care-IV). Databases were compared using a priori defined categories, including demographics, patient characteristics, and data richness. The study protocol and search strategy were prospectively registered.Data Synthesis: Four ICU databases fulfilled all criteria for inclusion and were queried using SQL (PostgreSQL version 12; PostgreSQL Global Development Group) and analyzed using R (R Foundation for Statistical Computing, Vienna, Austria). The number of unique patient admissions varied between 23,106 (AmsterdamUMCdb) and 200,859 (eICU-CRD). Frequency of laboratory values and vital signs was highest in HiRID, for example, 5.2 (±3.4) lactate values per day and 29.7 (±10.2) systolic blood pressure values per hour. Treatment intensity varied with vasopressor and ventilatory support in 69.0% and 83.0% of patients in AmsterdamUMCdb versus 12.0% and 21.0% in eICU-CRD, respectively. ICU mortality ranged from 5.5% in eICU-CRD to 9.9% in AmsterdamUMCdb.Conclusions: We identified four publicly available adult clinical ICU datasets. Sample size, severity of illness, treatment intensity, and frequency of reported parameters differ markedly between the databases. This should guide clinicians and researchers which databases to best answer their clinical questions. [ABSTRACT FROM AUTHOR]

Individuals and communities increasingly depend on, and fill their lives with, machine cultures, in the form of both interfaces and infrastructures. This global push for machine cultures has given rise to an increasing demand for data and engendered a proliferation of public, private and public-private dataset repositories. While datasets form a foundational element of machine cultures, they rarely come into focus as objects of critical study. But in recent years a critical discursive formation on datasets has begun to emerge, which disturbs the idea of datasets as operational instruments of digital knowledge production and seek to instead 'bring people back in'. The present article identifies these preliminary explorations as 'critical dataset studies' and draws on critical archival studies to articulate the ethico-political surfaced by these studies. Specifically it argues that critical dataset studies shows the need for an expanded ethical and conceptual approach to datasets that not only relies on linear notions of deletion and accountability but also on iterative frameworks of remains and response-ability. [ABSTRACT FROM AUTHOR]

In this article, we introduce a unique data set containing all written communication published by the German Bundestag between 1949 and 2017. Increasing numbers of scholars make use of protocols of parliamentary speeches, parliamentary questions or the texts of legislative drafts in various fields of comparative politics including representation, responsiveness, professionalization and political careers or parliamentary agenda studies. Since preparing parliamentary documents is rather resource intensive, these studies remain limited to single points in time, types of documents and/or policy areas. The long time horizon and various types of documents covered by our new comprehensive data set will enable scholars interested in parliaments, parties and representatives to answer various innovative research questions related to legislative studies. [ABSTRACT FROM AUTHOR]

With the rapid development of computer vision, human action recognition has shown its wide application prospects and research value in many fields such as video surveillance, video retrieval, and human-computer interaction. Human action recognition involves the understanding of image content, and the progress of practical applications is slow due to the complexity and diversity of human postures and the occlusion factors of the background. This paper comprehensively reviewed the development of human action recognition, and deeply explored the research methods in this field, including traditional manual feature extraction methods and deep learning-based methods, as well as the recently popular graph convolutional network（GCN）-based method. And this paper systematically summarized these methods according to the data types they used. In addition, for different data types, it introduced some popular action recognition datasets, compared and analyzed the performance of various methods on these datasets. Finally, this paper summarized the review, and prospected the future research direction of human action recognition. [ABSTRACT FROM AUTHOR]

A solution to the problem of low computational efficiency of the relational data model is investigated. An extension of the relational algebra with the help of operations over data sets, i.e., the basic construction of CODASYL DBTG proposals, is suggested. A user is able to choose a method for implementing data links depending on the following requirements for their processing speed: a slow but flexible option based on symbolic addressing typical of relational DBMSs, or a fast but hard option based on direct pointers (relative addressing) typical of DBMSs of the pre-relational era. [ABSTRACT FROM AUTHOR]

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository1. [ABSTRACT FROM AUTHOR]

Computer vision aims to produce an understanding of digital image's content and the generation or transformation of images through software. Today, a significant amount of computer vision algorithms rely on techniques of machine learning which require large amounts of data assembled in collections, or named data sets. To build these data sets a large population of precarious workers label and classify photographs around the clock at high speed. For computers to learn how to see, a scale articulates macro and micro dimensions: the millions of images culled from the internet with the few milliseconds given to the workers to perform a task for which they are paid a few cents. This paper engages in details with the production of this scale and the labour it relies on: its elaboration. This elaboration does not only require hands and retinas, it also crucially zes mobilises the photographic apparatus. To understand the specific character of the scale created by computer vision scientists, the paper compares it with a previous enterprise of scaling, Malraux's Le Musée Imaginaire, where photography was used as a device to undo the boundaries of the museum's collection and open it to an unlimited access to the world's visual production. Drawing on Douglas Crimp's argument that the "musée imaginaire", a hyperbole of the museum, relied simultaneously on the active role of the photographic apparatus for its existence and on its negation, the paper identifies a similar problem in computer vision's understanding of photography. The double dismissal of the role played by the workers and the agency of the photographic apparatus in the elaboration of computer vision foreground the inherent fragility of the edifice of machine vision and a necessary rethinking of its scale. [ABSTRACT FROM AUTHOR]

Atomic force microscope (AFM) is a powerful nanoscale instrument, which can obtain the true surface morphology of samples. There are strict requirements for the detailed features in AFM images, so it is necessary to mine the deep information in the images. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-quality images. For most cases, the original images of AFM are with low resolution. In order to get the more detailed texture and feature information as much as possible, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. By learning the mapping relationship between low-resolution images and high-resolution images from the image database, the high-resolution image is finally obtained. Aiming at the problem of long scanning time and too small training database of AFM image, adaptive histogram equalization is used to expand and enhance the training set of AFM images. Compared with the traditional super-resolution methods, the subjective and objective evaluation of the reconstructed image verifies the feasibility of the proposed algorithm. Aiming at the problem of long scanning time and too small training database of AFM image, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. It can save a lot of time and hardware cost by using adaptive histogram equalization (AHE) and improve the generalization ability of deep learning model to further improve the reconstruction quality of AFM image. [ABSTRACT FROM AUTHOR]

Façade parsing is an essential process before the 3-D modeling of digital or virtual 3-D city models. The existing grammar-based approaches for façade parsing rely on strong prior knowledge but can obtain façade parts with better structure. Pixelwise-segmentation-based approaches achieve façade parsing with much less knowledge but the resulting structure of façade parts is normally incomplete. Both these approaches are restricted by their high reliance on the data set. Therefore, they cannot be applied for façade parsing with complex scenes. To address this issue, we built a large street-level data set by taking Mapillary images as the training data for more general scenes. At the same time, we propose a new pipeline based on convolutional neural network (CNN) that combines pixelwise segmentation and global object detection to achieve better results for facade parsing. Our pipeline can be applied to façade images after rectification and street-level façade images with complex scenes. The result of the ablation study demonstrates that the design of our pipeline is effective. We test our pipeline on the classic ECP2011 data set and our new large street-level data set. Our pipeline achieves state-of-the-art results for both the data sets: an accuracy of 98.2% and the mean average precision (mAP) of 98.8% on the ECP2011 data set as well as the mAP of 81.1% for façade parts parsing on our street-level data set. [ABSTRACT FROM AUTHOR]

The growth and the harvestability of a broccoli crop is monitored by the size of the broccoli head. This size estimation is currently done by humans, and this is inconsistent and expensive. The goal of our work was to develop a software algorithm that can estimate the size of field-grown broccoli heads based on RGB-Depth (RGB-D) images. For the algorithm to be successful, the problem of occlusion must be solved, which is the partial visibility of the broccoli head due to overlapping leaves. This partial visibility causes sizing errors. In this research, we studied the use of deep-learning algorithms to deal with occlusions. We specifically applied the Occlusion Region-based Convolutional Neural Network (ORCNN) that segmented both the visible and the amodal region of the broccoli head (which is the visible and the occluded region combined). We hypothesised that ORCNN, with its amodal segmentation, can improve the size estimation of occluded broccoli heads. The ORCNN sizing method was compared with a Mask R–CNN sizing method that only used the visible broccoli region to estimate the size. The sizing performance of both methods was evaluated on a test set of 487 broccoli images with systematic levels of leaf occlusion. With a mean sizing error of 6.4 mm, ORCNN outperformed Mask R–CNN, which had a mean sizing error of 10.7 mm. Furthermore, ORCNN had a significantly lower absolute sizing error on 161 heavily occluded broccoli heads with an occlusion rate between 50% and 90%. Our software and data set are available on https://git.wur.nl/blok012/sizecnn. • The image-based size estimation of broccoli heads was studied. • A data set of 2560 broccoli images was acquired with systematic levels of occlusion. • Two deep-learning methods were tested on their ability to size occluded broccolis. • One deep-learning method used a dual segmentation to better deal with the occlusion. • The software and the data set have been made available. [ABSTRACT FROM AUTHOR]

Drought Prediction is to be focused of great importance for India. Drought can be Predicted using the amount of rainfall data. Two types of predictions can be done, they are – Long term Predictions: Predict rainfall over few weeks/months in advance. Short term Predictions: Predict rainfall a few days in advance in specific locations. Indian meteorological department provides forecasting data required for the project. In this Paper we are planning to work on long term predictions of rainfall. The main motive of the paper is to predict the amount of rainfall in Particular division and state whether the drought will occur or not in advance. We predict the amount of rainfall using Multiple Linear Regression(MLR) which is a branch of AI by using the past dataset. [ABSTRACT FROM AUTHOR]

Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry. Therefore, the present systematic review was conducted in 2019 to identify the minimum data set for a poisoning registry. Searches were performed in four scientific databases, i.e., PubMed, Scopus, Web of Science, and Embase. The keywords used in the searches included minimum data set, "poison", and "registry". Two researchers independently evaluated the titles, abstracts, and texts of the papers. The data were collected from the related papers. Ultimately, the minimum data set was identified for the poisoning registry. Data elements extracted from the sources were classified into two general categories: administrative data and clinical data. Ninety-eight data elements in the administrative data category were subdivided into three sections: general data, admission data, and discharge data. One-hundred and thirty-one data elements in the clinical data category were subdivided into five sections: clinical observation data, clinical assessment data, past medical history data, diagnosis data, and treatment plan data. The minimum data set is a prerequisite for creating and using a poisoning registry and data system. It is suggested to evaluate and use the poisoning minimum data set in accordance with the national laws, needs, and standards based on the opinion of the local experts. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data‐collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset—a high‐resolution task‐fMRI dataset acquired in a fixed environment—in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large‐scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants. [ABSTRACT FROM AUTHOR]

Object detection in images collected by Unmanned Aerial Vehicles (UAVs) constitutes a challenging task in computer vision, due to difficulties of learning a well-trained object detection model for handling instances in UAV images with arbitrary orientations, variation in different scales, irregular shapes, etc. In order to facilitate object detection research and extend its applications in natural scenarios by using UAVs, this paper presents a large-scale benchmark dataset, MOHR, aiming at performing multi-scale object detection in UAV images with high resolution. A total of 90,014 object instances with labels and bounding boxes were annotated. In order to build a baseline for object detection on the MOHR dataset, we performed an empirical study by evaluating six state-of-the-art deep learning-based object detection models trained on our proposed dataset. Experimental results show promising detection performance, but also demonstrate that the dataset is quite challenging for adopting natural image-based object detection models for UAV images. [ABSTRACT FROM AUTHOR]

The United Nations (UN) has developed a complex and interconnected system of committees, representatives, and missions in support of its peace and security mandate. This article introduces the United Nations Peace Initiatives (UNPI) data set, which provides information on 469 UN initiatives aimed at conflict prevention and crisis management, mediation, peacekeeping, and peacebuilding. The data encompass all initiatives mandated by the UN Security Council, the General Assembly, as well as Secretary General between 1946 and 2015. This includes diplomatic, technocratic, political-development, and peacekeeping missions. UNPI data provide an empirical basis to assess the relative contributions of various UN subsidiary bodies to prevent, manage, and suppress the outbreak and recurrence of conflict. This article discusses the underlying rationale of the data collection, the coding rules, and procedures, and shows how UNPI can be combined with conflict data. Initial analyses show the increased use of different types of UN peace initiatives over time. The UN regularly deploys multiple peace initiatives to a dispute, often with significant periods of overlap. Ongoing hostilities and economic development are found to be key determinants of mission choice. In line with the theme of the Special Issue, the UNPI data set underscores the importance of, and provides a tool through which to examine the, interdependencies between various conflict management efforts. [ABSTRACT FROM AUTHOR]

This article introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information, which can be useful for a wide range of tasks, including land cover classification, object counting, or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high-level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two data sets (using low- and high-resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The data sets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on convolutional neural networks (CNNs) for the visual part and a recurrent neural network (RNN) for the natural language part of this task. The model is trained on the two data sets, yielding promising results in both cases. [ABSTRACT FROM AUTHOR]

With the development of modern remote sensing technology, remote sensing images have become one of the powerful tools for people to understand the Earth and its surroundings. However, there is currently no good classification algorithm that can accurately classify images. In order to accurately classify remote sensing images, this paper studies the content of the article by using fuzzy C-means clustering algorithm and radial basis neural network (RBF). The classification accuracy of SIRI-WHU dataset was analyzed by using the classification accuracy evaluation index such as overall accuracy and Kappa coefficient. The Kappa coefficient of vegetation classification in SIRI-WHU dataset was 0.9678, and the overall accuracy reached 97.18%. According to the classification problem of remote sensing image, according to the characteristics of remote sensing image, the improved model Alex Net-10-FCM is used to classify the remote sensing image dataset, and very high classification accuracy is obtained. [ABSTRACT FROM AUTHOR]

Traditional point cloud registration methods require large overlap between scans, which imposes strict constraints on data acquisition. To facilitate registration, users have to carefully position scanners to ensure sufficient overlap. In this article, we propose to use high-level structural information (i.e., plane/line features and their interrelationship) for registration, which is capable of registering point clouds with small overlap, allowing more freedom in data acquisition. We design a novel plane-/line-based descriptor dedicated to establishing structure-level correspondences between point clouds. Based on this descriptor, we propose a simple but effective registration algorithm. We also provide a data set of real-world scenes containing a larger number of scans with a wide range of overlap. Experiments and comparisons with state-of-the-art methods on various data sets reveal that our method is superior to existing techniques. Though the proposed algorithm outperforms state-of-the-art methods on the most challenging data set, the point cloud registration problem is still far from being solved, leaving significant room for improvement and future work. [ABSTRACT FROM AUTHOR]

Objective: Children with cerebral palsy experience functional limitations because of excessive muscle weakness, spasticity, and impaired motor control. They are prescribed ankle-foot orthoses to assist with ambulation. Our objective was to analyze stride length and dorsiflexion data comparing the effectiveness of "ankle-foot orthoses" with "barefoot or shoes only" on ambulatory children with cerebral palsy. Design: An electronic literature search was conducted. Studies were screened by two reviewers based on our inclusion criteria: prospective cohort study or randomized clinical trial, participants younger than 18 yrs with a primary diagnosis of cerebral palsy, ankle-foot orthoses with a control group, 20 combined participants in the experimental and control groups for cohort studies, and 10 participants for randomized clinical trials. Results: Seventeen studies were selected. Pooled results of the meta-analyses showed that stride length was significantly better in the ankle-foot orthoses group as compared with the control group (mean difference between groups = 0.05 m (95% confidence interval = 0.04-0.06). The dorsiflexion angle (5 studies, 124 participants) was improved in patients with ankle-foot orthoses as compared with barefoot or shoes only (mean difference between groups = 8.62 degrees (95% confidence interval = 8.05-9.2). Conclusions: Children with cerebral palsy using ankle-foot orthoses had improved stride length and dorsiflexion angle during gait in a pooled meta-analyses of cohort studies and clinical trials. [ABSTRACT FROM AUTHOR]

Background: Inadequate description of palliative care cancer patients in research studies often leads to results having limited generalizability. To standardize the description of the sample, the European Association for Palliative Care basic data set was developed, with 31 core demographic and disease-related variables. Aim: To pilot test the data set to check acceptability, comprehensibility and feasibility. Design: International, multi-centre pilot study at nine study sites in five European countries, using mixed methods. Setting/participants: Adult cancer patients and staff in palliative care units, hospices and home care. Results: In all, 191 patients (544 screened) and 190 health care personnel were included. Median time to fill in the patient form was 5 min and the health care personnel form was 7 min. Ethnicity was the most challenging item for patients and requires decisions at a national level about whether or how to include. Health care personnel found weight loss, principal diagnosis, additional diagnoses and stage of non-cancer diseases most difficult to respond to. Registration of diagnoses will be changed from International Statistical Classification of Diseases and Related Health Problems, 10th version code to a predefined list, while weight loss and stage of non-cancer diseases will be removed. The pilot study has led to rewording of items, improvement in response options and shortening of the data set to 29 items. Conclusion: Pilot testing of the first version of the European Association for Palliative Care basic data set confirmed that patients and health care personnel understand the questions in a consistent manner and can answer within an acceptable timeframe. The pilot testing has led to improvement, and the new version is now subject to further testing. [ABSTRACT FROM AUTHOR]

PURPOSE: To describe the medical complexity of traumatic spinal cord injury (TSCI) in paediatric patients in Western Australia (WA). Secondly, to determine if Princess Margaret Hospital (PMH) for Children (the tertiary paediatric centre in WA where all TSCI patients are managed) is meeting the requirements of the Australasian Rehabilitation Outcomes Centre (AROC) paediatric rehabilitation minimum data set gathered on each patient. METHODS: Retrospective cohort study of patients seen at PMH between 1996–2016. The AROC minimum dataset information data were gathered on each patient. Functional status and rehabilitation outcomes were assessed using Functional Independence Measure for Children (weeFIM), Canadian Occupational Performance Measure (COPM), and Goal Attainment Scaling (GAS). Patient complexity was captured by documenting the specialty teams involved, the number of readmissions, and the International Statistical Classification of Disease and Related Health Problems Z codes. RESULTS: Data from 19 patients (13 males, age range 6 months-15 years; 6 females, age range 4 years-13 years) were available. There were 10 cervical TSCIs with a median length of stay of 213 days and 9 thoracic TSCIs with a median length of stay of 49 days. Patients had between zero and six comorbidities prior to their TSCI. CONCLUSIONS: Children with medical complexity are responsive to rehabilitation but have a high burden of care, requiring multiple-specialty care and hospital re-admissions. AROC has set a minimum data set recommendation for the collection and examination of patient data. PMH meets the AROC guidelines for patient data collection and descriptive analyses. [ABSTRACT FROM AUTHOR]

This technical report documents the details of Aurorasaurus citizen science data for the period spanning 2015 and 2016 as well as its routine data filtering protocols. Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms when auroral precipitation models have the highest uncertainty. These data are offered to the scientific community for use through an open‐access database in its raw and scientific formats, each of which is described in detail in this technical report. Furthermore, by demonstrating its scientific utility, we aim to encourage its integration into auroral research. Key Points: Newly available Aurorasaurus database offers quality‐controlled, citizen science, and social media reports of auroraThe breadth of scientific utility of Aurorasaurus data is demonstratedCrowd‐sourced aurora data is provided for model validation efforts of the space weather research community [ABSTRACT FROM AUTHOR]

Abstract Background To define a core set of geriatric data to be methodically collected in clinical cancer trials of older adults, enabling comparison across trials. Patients and methods Following a consensus approach, a panel of 14 geriatricians from oncology clinics identified seven domains of importance in geriatric assessment. Based on the international recommendations, geriatricians selected the mostly commonly used tools/items for geriatric assessment by domain (January–October 2015). The Geriatric Core Dataset (G-CODE) was progressively developed according to RAND appropriateness ratings and feedback during three successive Delphi rounds (July–September 2016). The face validity of the G-CODE was assessed with two large panels of health professionals (55 national and 42 international experts) involved both in clinical practice and cancer trials (March–September 2017). Results and discussion After the last Delphi round, the tools/items proposed for the G-CODE were the following: (1) social assessment: living alone or support requested to stay at home; (2) functional autonomy: Activities of Daily Living (ADL) questionnaire and short instrumental ADL questionnaire; (3) mobility: Timed Up and Go test; (4) nutrition: weight loss during the past 6 months and body mass index; (5) cognition: Mini-Cog test; (6) mood: mini-Geriatric Depression Scale and (7) comorbidity: updated Charlson Comorbidity Index. More than 70% of national experts (42 from 20 cities) and international experts (31 from 13 countries) participated. National and international surveys showed good acceptability of the G-CODE. Specific points discussed included age-year cut-off, threshold of each tool/item and information about social support, but no additional item was proposed. Conclusion We achieved formal consensus on a set of geriatric data to be collected in cancer trials of older patients. The dissemination and prospective use of the G-CODE is needed to assess its utility. Highlights • The Geriatric Core Dataset (G-CODE) is a minimum set of geriatric data to be collected in cancer trials. • The G-CODE was developed after a Delphi consensus method with international agreement. • The G-CODE has ten tools/items and a median completion time of 9 min. • The G-CODE allows for a minimal geriatric description of the older cancer population. • The G-CODE allows standardisation of geriatric data, enabling comparison across trials. [ABSTRACT FROM AUTHOR]

Highlights • Lung cancer cases presented to multidisciplinary teams (MDT) may differ from those that are not. • Differences include age, stage and ECOG status. • Despite this, MDT presentation is associated with improved lung cancer survival at 1, 2 and 5 years. • Institutional cancer registry data can support important analysis of local outcomes. Abstract Background Multidisciplinary team (MDT) presentation in lung cancer has the potential to improve longterm outcomes, although this varies between studies. This study aims to evaluate outcomes including survival, according to MDT presentation and to explore the utility of data obtained from local clinical sources. Patients and methods Prospective cases of lung cancer recorded in our institution's cancer registry were analyzed according to MDT presentation for patient and tumour characteristics, adjusted survival and referral to palliative care. Results 1197 cases were included, 295 (24.6%) with MDT presentation and 902 (75.4%) without. 60% of patients were male with median (IQR) age at diagnosis of 70 years (62–78). Histopathology distribution (non-small cell lung cancer and small-cell lung cancer) was similar between the two groups. Compared with the non-MDT group, the MDT group had (1) ECOG score recorded more often (71.9% vs. 47.6%), (2) higher proportion of ECOG 0 cases (31.2% vs. 11.9%) and ECOG 1 cases (28.8% vs. 20.3%), (3) higher proportion of early stage disease (stage I - 23.1% vs. 9.7% stage II - 10.2% vs. 4.8%, stage IIIA - 14.6% vs 6.3%) and (4) lower proportion of metastatic disease (stage IV - 39.3% vs. 56.1%). Referral to palliative care was incompletely recorded in both groups (MDT: n = 116/295, 39.3%; non-MDT: n = 430, 47.7%) but did not differ significantly for stage IV cases. Survival analyzed by stage was greater in the MDT group at 1, 2 and 5 years for all stages except stage IIIB at 1 year post-diagnosis. Adjusted survival analysis for the entire cohort showed improved survival at 5 years for the MDT group (HR 0.7 (0.58-0.85), p < 0.001). Conclusion MDT presentation is associated with improved adjusted survival for lung cancer in this single institutional cohort in an analysis of local clinical cancer registry data. [ABSTRACT FROM AUTHOR]

The traditional data query algorithm based on clustering strategy library ignores the association features of social network data, characteristic data acquisition exist a large number of redundant features and frequent relationship among features is low, resulting in the social network data query efficiency and the accuracy is poor, so a fast query algorithm for social network data based on fuzzy degree function based on association features is proposed, it is based on Apriori algorithm for data association feature mining of social network to obtain the maximum frequent association feature set; for association feature preprocessing, it reduce the maximum frequent association feature set by feature dimension reduction and de redundancy algorithm, to obtain better social network maximal frequent associated feature set; when using fuzzy function to query social network data quickly, it uses data of a single gene ambiguity function to build a fast data query diagram, input the best frequent feature set of social network, and output the query results of social network data with the highest priority. The experimental results show that the proposed algorithm has the advantages of high efficiency and high accuracy in social network data query. [ABSTRACT FROM AUTHOR]

For rigorous empirical study of religion to move forward, improved data on religious demographics of states' populations, with more extensive and consistent coverage, become essential. The Religious Characteristics of States (RCS) data set project provides annual data on religious demographics for ninety-seven denominations, from 2010 back to as early as 1800 (approximately 6.7 million data points). Unlike existing data sets, RCS matches the temporal coverage of the Correlates of War and Polity data sets and is structured for easy merger with them. Illustrations of RCS's capabilities include measurements of religions' associations with regime type, wealth, and conflict. Religious characteristics emerge as significant state-level variables and rival other factors conventionally thought to influence political processes and outcomes. The results encourage further research using RCS on a wide range of problems within international relations and comparative politics. [ABSTRACT FROM AUTHOR]