The Dimensions API’s flexible query syntax and native json results format integrate well with data science workbenches such as Jupyter notebooks. (Jupyter notebook and HTML rendering files attached).

COVID articles per day based on the dimensions search for COVID-19

Research outputs results from querying with the boxed COVID search definition in Dimensions. Outputs are grouped week by week and are not shown cumulatively. Output types as per the legend

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper’s method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy.

matlab result of measurement of VSMCs, orientation is originally from matlab, should be converted into anlge defined in the article

Dimensions of output and the variables used to operationalize the dimensions.

List of Top Authors of Human Dimensions of Wildlife sorted by article citations.

Evaluation of these methods are performed in the same manner as in Table 1. Data are simulated under Model Split with the size of each subpopulation reduced from 50 to 10 and the number of loci reduced from 100 to 10, respectively.

Dimensions of data quality.

This file contains all relevant publications, datasets and clinical trials from Dimensions that are related to COVID-19. The content has been exported from Dimensions using a query in the openly accessible Dimensions application, which you can access at https://covid-19.dimensions.ai/. Dimensions is updated once every 24 hours, so the latest research can be viewed alongside existing information. With its range of research outputs including datasets and clinical trials, both of which are just as important as journal articles in the face of a potential pandemic, Dimensions is a one-stop shop for all COVID-19 related information. Please share this information with anyone you think would benefit from it. If you have any suggestions as to how we can improve our search terms to maximise the volume of research related to COVID-19, please contact us at support@dimensions.ai.

Dimensions of authenticity.

This dataset is a cleaned set of data from the myPersonality Project which focuses on data from the IPIP and Hofstede Cultural Dimensions.

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen’s kappa) and agreement across all datasets (Fleiss’ kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.

ABSTRACT Although big data has become an integral part of businesses and society, there is still concern about the quality aspects of big data. Past research has focused on identifying various dimensions of big data. However, the research is scattered and there is a need to synthesize the ever involving phenomenon of big data. This research aims at providing a systematic literature review of the quality dimension of big data. Based on a review of 17 articles from academic research, we have presented a set of key quality dimensions of big data.

ARWU sub-dimensions and their corresponding weights.

THEWUR sub-dimensions and their corresponding weights.

Journal publication results from querying with the boxed COVID search definition in Dimensions. Open Access classification derived from Unpaywall. Outputs are grouped month by month and are not shown cumulatively. Open Access mode as per the legend.

Comparative results for 10 dimensions.

Classes of spatialization ranked according to their proportion of use with respect to the total number of mapping occurrences involving Spatialization (A17). Significantly higher percentages () are indicated with a star (*).

Items fitting in 2 domains are included as “+” with the respective second domain. Acronyms are explained in Table 4.