Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “Rule-base”
Experimental HIL datasets of a heat pump controlled by MPC or rule-based controllers for energy flexibility
<p>Hardware-in-the-loop experiment performed in the SEILAB laboratory of IREC<br> Air-to-water heat pump including a DHW tank for production of SH and DHW, which external unit is placed in a climate chamber that reproduces the desired weather conditions dynamically<br> Control is MPC or rule-based, both triggered either by a signal of price or CO2 intensity from the grid (4 series of experiments)<br> Connected to virtual residential building (flat) in Spanish Mediterranean climate<br> More information:<br> https://doi.org/10.1109/ACCESS.2019.2903084</p>
Figure 5. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The behavior module determines the target position and orientation according to the results<br> of localization and the sensor measurements, and then constructs an action series which consists of<br> the elementary gaits to realize omni directional walking. The implementation of forward walking is<br> applying Virtual Slope Walking in the sagittal plane with the Lateral Swing Movement for lateral<br> stability. The sideward walking and turning is realized by carefully designing the key frames. All of<br> above gait is generated by connecting the key frames with smooth sinusoids. The forward walking<br> speed of PERSIA Humanoid Robot is 25cm/s. The image sequences of forward walking are shown<br> in Figure 5.</p>
Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 2 shows one of the constructions used for our robots. Knee joints are considered to<br> bend in both directions which help faster response of the robot in backward walking. Efforts have<br> been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall<br> and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head.<br> To facilitate exchange of the players, all robots use mechanically the same structure.</p>
Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 4 shows the block diagram of the software which runs in the robot’s main processor.<br> The program consists of 4 main blocks:<br> • Hardware Interface: Contains all low level routines to access hardware of the robot including<br> sensors and actuators.<br> • Vision: Contains image processing algorithms such as recognition of landmarks and other<br> object. Self localization is done using particle filtering. Particles are scored by comparing a<br> simulated image from each particle with the current frame captured by camera. Using<br> “Sampling-Importance Resampling” method, a new distribution of the particles is created after<br> each step.<br> Particles are also updated using a motion model. Final distribution of the particles converges to<br> the real pose of the robot.<br> • Planning: Planning system of the robot is based on a multi layer, and multi thread structure.<br> The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario<br> which runs in parallel with the scenarios in the other layers. A scenario in a higher level can<br> terminate and change the scenario running in the lower level; however it is usually done in<br> synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as<br> stopping the walking motion while one of the feet is still in the air).<br> • Network: Mainly responsible for the wireless communication of the robot with the other robots<br> or the referee box. This is done via WLAN.<br> • Motion Control: manages all the actuators of the robot, and controls locomotion or any other<br> action of the robot according to the requests from Cognition.<br> • Sensor Control: manages other sensors, and interacts with the Sub-Controller.</p>
Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>This module receives information from Artificial Intelligent unit. Total functions about<br> Robot Behavior such as stability motors actions, robot path planning, turn camera, walking,<br> shooting, dribbling; motion and etc are controlled in this section.</p>
Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>In this paper, we will at first describe the general hardware design of the PERSIA Humanoid<br> Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and<br> learning (section 3). Finally, section 4 concludes this paper. This document describes the current<br> state of the project as well as the intended development for the RoboCup 2010 competition.</p>
Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The PERSIA Humanoid robot designed for has multipurpose capability. This robot<br> equipped with main board for motion control, vision sensor, other balancing sensors, servo motors<br> and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control<br> system.</p>
USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search
<p>USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).</p><p>This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. </p><p>To use parquet in Pandas, install PyArrow and fastparquet using pip:</p><p>pip install pyarrow==13.0<br>pip install fastparquet</p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
Rule-based generative design of translational and rotational interlocking assemblies
<p>Video illustrating the ideas and limitations developped in our eponymous article.</p>
Rule-based deconstruction and reconstruction of diterpene libraries: Categorizing foundational patterns & unravelling the structural landscape
Open the record for dataset details and reuse information.
Scheib_Stoll_Randerath_Frontiers_Does Aging Amplify the Rule-based Efficiency Effect in Action Selection_Dataset
<p>Dataset belongs to publication in Frontiers in Psychology, section Cognition</p> <p>Title: Does Aging Amplify the Rule-based Efficiency Effect in Action Selection?<br> Authors: Jean P. P. Scheib 1; Sarah E. M. Stoll 1,2; Jennifer Randerath 3,2,1</p> <p>Affiliations:<br> 1: University of Konstanz, Konstanz, Germany,</p> <p>2: Lurija Institute for Rehabilitation Science and Health Research, Kliniken Schmieder, Allensbach, Germany</p> <p>3: University of Vienna, Vienna, Austria</p>
Rule-based Synthetic Data for Japanese GEC
<pre>Title: Rule-based Synthetic Data for Japanese GEC <strong>Dataset Contents:</strong> This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows: <strong>Synthetic Corpus - *synthesized_data.tsv*</strong> This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction. These paired sentences are derived from data scraped from the keyword-lookup site <yourei.jp>. The data within this file is primarily intended to serve as or augment a training set for a Japanese GEC model. Overall the sentences cover a broad array of primarily simple Japanese grammatical errors. <strong>Teacher Corpus - *teacher_data.tsv*</strong> This corpus file contains 6,345 parallel sentence pairs created via what we call the "teacher-sourcing" project [2]. The corpus sentences were created by Japanese language teachers, and this "teacher-sourcing" was funded by the Japan Foundation, Los Angeles. The overall format of the file is similar to that of *synthesized_data.tsv*, with each line containing an erroneous sentence and a corresponding correction separated by a comma. In addition, each erroneous sentence and correction sentence also contain pairs of characters that delimit the specific location within the sentence where the error/correction occur. For the erroneous sentence, these characters are `<` and `>`, while for the correction sentence, these are `(` and `)`. For example, consider the following sentence pair: - Error: <汚れる服>をあらいました。 - Correction: (汚れた服)をあらいました。 The delimiter characters indicate that the error phrase is `汚れる服` while the corresponding correction is `汚れた服` These paired sentences were written to mimic commonly grammatical errors produced by Japanese langauge learners; thus this file's data is primarily intended to serve as a evaluation set for Japanese GEC models. ______ In addition, the dataset contains the rule file used to generate the synthetic data within *synthesized_data.tsv*: ### Rule File - *rule_set.tsv* This file contains the 400 "syntactic rules" used to generate the data within *synthesized_data.tsv*. Each line contains a single rule, with different attributes delimited by tabs. Consult pages 41-66 of [1] for a more detailed analysis of these "syntactic rules" and the manner in which they are used to produce the synthetic data. </pre>
Data from: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme
With the rapid increase in social networks and blogs, the social media services are increasingly being used by online communities to share their views and experiences about a particular product, policy and event. Due to economic importance of these reviews, there is growing trend of writing user reviews to promote a product. Nowadays, users prefer online blogs and review sites to purchase products. Therefore, user reviews are considered as an important source of information in Sentiment Analysis (SA) applications for decision making. In this work, we exploit the wealth of user reviews, available through the online forums, to analyze the semantic orientation of words by categorizing them into +ive and -ive classes to identify and classify emoticons, modifiers, general-purpose and domain-specific words expressed in the public's feedback about the products. However, the un-supervised learning approach employed in previous studies is becoming less efficient due to data sparseness, low accuracy due to non-consideration of emoticons, modifiers, and presence of domain specific words, as they may result in inaccurate classification of users' reviews. Lexicon-enhanced sentiment analysis based on Rule-based classification scheme is an alternative approach for improving sentiment classification of users' reviews in online communities. In addition to the sentiment terms used in general purpose sentiment analysis, we integrate emoticons, modifiers and domain specific terms to analyze the reviews posted in online communities. To test the effectiveness of the proposed method, we considered users reviews in three domains. The results obtained from different experiments demonstrate that the proposed method overcomes limitations of previous methods and the performance of the sentiment analysis is improved after considering emoticons, modifiers, negations, and domain specific terms when compared to baseline methods.
Rule-Based Closed Loop System for Type 1 Diabetes Control
ClinicalTrials.gov study NCT01614496. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Feasibility and Pilot Study of a Rule-based Chatbot Application for Adolescents With Anxiety Symptoms
ClinicalTrials.gov study NCT05758935. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme
Open the record for dataset details and reuse information.
Study of Rule-Based Algorithms in Anemia Management in Prevalent Hemodialysis Patients and Its Relation to Patients' Outcomes
ClinicalTrials.gov study NCT06996717. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset related to article "Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis"
<p>This record contains raw data related to article "Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis"</p><p>Abstract</p><p>Background: The impact of a multidisciplinary management of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis on systemic glucocorticoids or innovative treatments remains unknown. Rule-based natural language processing and text extraction help to manage large datasets of unstructured information and provide insights into the profile of treatment choices.</p><p>Methods: We obtained structured information from text data of outpatient visits between 2017 and 2022 using regular expressions (RegEx) to define elastic search patterns and to consider only affirmative citation of diseases or prescribed therapy by detecting negations. Care processes were described by binary flags which express the presence of RA, PsA and psoriasis and the prescription of glucocorticoids and biologics or small molecules in each cases. Logistic regression analyses were used to train the classifier to predict outcomes using the number of visits and the other specialist visits as the main variables.</p><p>Results: We identified 1743 patients with RA, 1359 with PsA and 2,287 with psoriasis, accounting for 5,677, 4,468 and 7,770 outpatient visits, respectively. Among these, 25% of RA, 32% of PsA and 25% of psoriasis cases received biologics or small molecules, while 49% of RA, 28% of PsA, and 40% of psoriasis cases received glucocorticoids. Patients evaluated also by other specialists were treated more frequently with glucocorticoids (70% vs. 49% for RA, 60% vs. 28% for PsA, 51% vs. 40% for psoriasis; p < 0.001) as well as with biologics/small molecules (49% vs. 25% for RA, 64% vs. 32% in PsA; 51% vs. 25% for psoriasis; p < 0.001) compared to cases seen only by the main specialist.</p><p>Conclusion: Patients with RA, PsA, or psoriasis undergoing multiple evaluations are more likely to receive innovative treatments or glucocorticoids, possibly reflecting more complex cases.</p><p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.