CSV
Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.
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This dataset was created for an Eli Lilly and Company employee information management training program. It was part of a project that explored the potential use of ClinicalTrials.gov (CT.gov) as a tool to evaluate the severity of changes to inclusion and exclusion criteria on clinical trial operations. CT.gov is a public clinical study registry that records summary data about clinical trials. The registry includes a historical record of changes (change history) to inclusion and exclusion criteria and other data that is accessible by users.
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Water consumption. Data recorded between 2017.1.1 and 2019.12.31.
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It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project.
In addition, we provide synthetic data for a mixture of isotropic distributions.
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Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". CSV files with dataset results summaries, the evaluated sentences, detailed results, and scores. Results data contains training and evaluation ARFF files for each user, containing features of synthetic and legitimate samples as described in the article. The source data comes from three free text keystroke dynamics datasets used in previous studies, by the authors (LSIA) and two other unrelated groups (KM, and PROSODY, subdivided in GAY, GUN, and REVIEW).
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These datasets collect sensorial information about collaborative robot functioning. We recorded information from two different kinds of robots UR3e and UR10e. This dataset is used for data-driving modeling of the power consumption of cobots. The datasets have the following information: recording time, trajectory ID, joints' positions, joints' velocities, motor currents, motor torques, motor voltages, end effector position, force and momentum exerted to the end effector, current and voltage of the robot.
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Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". Source data contains CSV files with dataset results summaries, false positives lists, the evaluated sentences, and their keystroke timings. Results data contains training and evaluation ARFF files for each user and sentence with the calculated Manhattan and euclidean distance, R metric, and the directionality index for each challenge instance.
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Twitter is one of the most popular social networks for sentiment analysis. This data set of tweets are related to the stock market. We collected 943,672 tweets between April 9 and July 16, 2020, using the S&P 500 tag (#SPX500), the references to the top 25 companies in the S&P 500 index, and the Bloomberg tag (#stocks). 1,300 out of the 943,672 tweets were manually annotated in positive, neutral, or negative classes. A second independent annotator reviewed the manually annotated tweets.
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