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1,719 results for “Making”
Data of paper entitled "The Impact of Financial Influencers, Social Influencers, and FOMO Economy on the Decision-Making of Investment on Millennial Generation and Gen Z of Indonesia"
<p>The repository contains:</p> <ol> <li>Result of inner model of PLS</li> <li>Result of outer model of PLS</li> <li>Questionnaire result</li> </ol>
Fig. 1 in Haemoprotozoa: Making biological sense of molecular phylogenies
Fig. 1. Key characteristics of the five haemoprotozoan assemblages.
Making Sense Barcelona Noise Pilot Datasets
<p><strong>Raw Datasets From the Making Sense Barcelona Noise Pilot captured by the community using the Smart Citizen Kit 1.5</strong></p> <p> </p> <p>Data archived from the Smart Citizen Plaform:</p> <p><a href="https://smartcitizen.me/kits/4294">https://smartcitizen.me/kits/4294</a> <a href="https://smartcitizen.me/kits/4293">https://smartcitizen.me/kits/4293</a> <a href="https://smartcitizen.me/kits/4282">https://smartcitizen.me/kits/4282</a> <a href="https://smartcitizen.me/kits/4283">https://smartcitizen.me/kits/4283</a> <a href="https://smartcitizen.me/kits/4271">https://smartcitizen.me/kits/4271</a> <a href="https://smartcitizen.me/kits/4307">https://smartcitizen.me/kits/4307</a> <a href="https://smartcitizen.me/kits/4309">https://smartcitizen.me/kits/4309</a> <a href="https://smartcitizen.me/kits/4308">https://smartcitizen.me/kits/4308</a> <a href="https://smartcitizen.me/kits/4301">https://smartcitizen.me/kits/4301</a> <a href="https://smartcitizen.me/kits/4302">https://smartcitizen.me/kits/4302</a> <a href="https://smartcitizen.me/kits/4262">https://smartcitizen.me/kits/4262</a> <a href="https://smartcitizen.me/kits/4265">https://smartcitizen.me/kits/4265</a> <a href="https://smartcitizen.me/kits/4281">https://smartcitizen.me/kits/4281</a> <a href="https://smartcitizen.me/kits/4280">https://smartcitizen.me/kits/4280</a> <a href="https://smartcitizen.me/kits/4267">https://smartcitizen.me/kits/4267</a> <a href="https://smartcitizen.me/kits/4300">https://smartcitizen.me/kits/4300</a> <a href="https://smartcitizen.me/kits/4299">https://smartcitizen.me/kits/4299</a> <a href="https://smartcitizen.me/kits/4296">https://smartcitizen.me/kits/4296</a> <a href="https://smartcitizen.me/kits/4295">https://smartcitizen.me/kits/4295</a> <a href="https://smartcitizen.me/kits/4297">https://smartcitizen.me/kits/4297</a> <a href="https://smartcitizen.me/kits/4298">https://smartcitizen.me/kits/4298</a> <a href="https://smartcitizen.me/kits/4468">https://smartcitizen.me/kits/4468</a> <a href="https://smartcitizen.me/kits/4470">https://smartcitizen.me/kits/4470</a></p> <p>You can also explore the data using the Smart Citizen API:</p> <p><a href="https://developer.smartcitizen.me/">https://developer.smartcitizen.me/</a></p>
EUCROWD - Citizen's crowdsourcing in politics and policy-making
<p>TRILLION project and platform presentation as a case presentation on citizen's crowdsourcing in politics and policy-making.</p>
The Decision-Making Process Behind Library Adoption
<p>This is the Factors/Process Book for the research paper "The Decision-Making Process Behind Library Adoption", submitted to: The 35th IEEE International Conference on Software Maintenance and Evolution (ICSME).</p>
Dataset from: "The possibility to make choices modulates feature-based effects of reward"
<p>Dataset from the following publication:</p> <p>Heuer, A., Wolf, C., Schütz, A. C., & Schubö, A. (2019). The possibility to make choices modulates feature-based effects of reward. <em>Scientific Reports,</em> 9:5749.</p>
The Decision-Making Process Behind Software Library Adoption
<p>Dataset supporting journal publication "The Decision-Making Process Behind Software Library Adoption".</p>
Training Data for "Making sense of a newly assembled genome"
<p>The data provided here is part of the Galaxy Training Network tutorial for Making sense of a newly assembled genome. This data was sourced from NCBI on 2019-08-29</p>
Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data
<p>Corresponding data set for Tran-SET Project No. 18PLSU08. Abstract of the final report is stated below for reference:</p> <p>"Automated and timely road pavement damage inspection is critical to the preventive maintenance and the long-term sustainability and resilience of roads in Region 6. Current road inspection practices rely heavily on a manual process. Sensor-based methods (e.g., LiDAR scanning) are promising but can be too expensive for a wider adoption. This study employs a crowdsourcing approach of using the vibration patterns of regular vehicles in inferring specific types of road damages. A cloud-based smart phone app and system was developed to collect real-time vehicle vibrations, location data, and road damage images for training the detection model. However, there is a great challenge in using classic classification methods with crowdsourced vibration data containing high level of noises, as vehicle vibrations are greatly affected by the types and conditions of the vehicles, as well the varying driving behaviors of drivers. The study thus employed the recent developments in Deep Learning methods, including a Self-Taught Learning (STL) algorithm and Sparse Coding to tackle with the low-quality issues of collected data. A total of 310 miles of road-induced vehicle vibration data was collected in Texas and Louisiana, and the road damage detection model was trained on Texas A&M University (TAMU) supercomputing server. The results show that the features generated from Sparse Coding greatly contribute to enhancing detection performances, by addressing low-quality data issues."</p>
Fig. 17.5 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"
Fig. 17.5. Three examples of bodysize evo
How to select predictive models for decision making or causal inference? Experiments data
<p>This is the full result data for the experiments of the paper : Doutreligne, M., & Varoquaux, G. (2023). How to select predictive models for decision making or causal inference?, https://hal.science/hal-03946902. <br><br>The code repository is : https://github.com/soda-inria/caussim/tree/main</p> <p>The files in this dataset are the one for the most computationnally costly experiments. There is one folder for each of the four datasets used in the paper. Then, one folder for each of the experimental setup. The files required for the main figure (Fig.3) of the paper are the one labelled #fig3 in the following descriptions.</p> <p>Details on the files : </p> <p>.<br>├── acic_2016_save<br>│ ├── acic_2016__nuisance_non_linear__candidates_hist_gradient_boosting__dgp_1-77__rs_1-5<br>│ │ └── run_logs.csv: results for the experiment with non linear models for both the nuisances and the candidates<br>│ ├── acic_2016__nuisance_non_linear__candidates_ridge__dgp_1-77__rs_1-10<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates<br>│ └── acic_2016__stacked_regressor__dgp_1-77__seed_1-10<br>│ └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3<br>├── acic_2018_save<br>│ └── acic_2018__nuisance_non_linear__candidates_hist_gradient_boosting__first_uid_432<br>│ └── run_logs.csv results for the experiment with stacked models (linear and non linear) for the nuisances models and non linear models for the candidates #fig3<br>├── caussim_save<br>│ ├── caussim__linear_regressor__test_size_5000__n_datasets_1000<br>│ │ ├── run_logs.csv: results for the experiment with stacked models for the nuisances models and linear models for the candidates <br>│ │ └── simu.yaml: configuration file of the experiment<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_join_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, joined sets for the nuisances and the candidates<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_separated_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, separated sets for the nuisances and the candidates<br>│ └── caussim__stacked_regressor__test_size_5000__n_datasets_1000<br>│ ├── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and linear models for the candidates #fig3<br>│ └── simu.yaml: configuration file of the experiment<br>└── twins_save<br> └── twins__stacked_regressor__rs_1-10__overlap_0.1-3<br> └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3</p>
Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications: Example Scenario with Comments
<p>This example scenario with comments has been created in conjunction with the report “A Framework for Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications” to be published by the IEEE Standards Association (IEEE SA) Research Group on Issues of AI and Autonomy in Defense Systems (the Research Group). It provides insights into the Research Group's working methods and process.</p>
Dataset for "Dissociable Roles of the mPFC-to-VTA pathway in the control od Impulsive action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats"
<p>This dataset corresponds to the study "Dissociable Roles of the mPFC-to-VTA Pathway in the Control of Impulsive Action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats". In this study, we used Positron Emission Tomography with [18F]-Fluorodeoxyglucose to evaluate brain metabolic activity in Roman High- (RHA) and Low-avoidance (RLA) rats, which exhibit innate differences in impulsivity. Notably, we used a viral-based intersectional chemogenetic strategy to isolate the role of the mPFC-to-VTA pathway in controlling impulsive behaviors. We selectively activated the mPFC-to-VTA pathway in RHA rats and inhibited it in RLA rats, assessing the effects on impulsive action and RDM in the rat gambling task. Our results showed that RHA rats displayed higher impulsive action, less optimal decision-making, and lower cortical activity than RLA rats at baseline. Chemogenetic activation of the mPFC-to-VTA pathway reduced impulsive action in RHA rats, whereas chemogenetic inhibition had the opposite effect in RLA rats. However, these manipulations did not affect RDM. Our findings suggest a dissociable role of the mPFC-to-VTA pathway in impulsive action and RDM, highlighting its potential as a target for investigating impulsivity-related disorders.</p> <p><strong>Contributions for usage of this data in publications:</strong></p> <p>If you publish any work using these data, please cite this repository and the associated publication.</p>
Cellular crowd control: overriding endogenous cell coordination makes cell migration more susceptible to external programming
<p>The uploaded files include 1) raw data used to generate plots and graphs included in the manuscript, 2) the total raw dataset from all experiments, 3) representative raw videos per experimental condition and data corresponding to each of the videos, and 4) Matlab scripts written by the researchers used for data analysis. </p>
The crater detection results (necessary data for making Figs 6 and 7) for areas A2, A4 and A7 to estimate the ages of RMDS-overlapped craters in Figs 2a-2c
<p>To estimate the ages of RMDS-overlapped craters in Figs 2a-2c ( located in areas A2, A4 and A7, respectively) with a locally calibrated Monte Carlo model, the NAC-DEM-based crater detection results are provided here, as the basis input and necessary data to make Figs 6 and 7.</p> <p>They have been introduced in the following publication, where more details can be found:</p> <p>F. Zhang, J. W. Head, C. Wöhler, A. T. Basilevsky, L. Wilson, M. Xie, R. Bugiolacchi, T. Wilhelm, S. Althoff, Y. L. Zou.</p> <p>The Lunar Mare Ring-Moat Dome Structure (RMDS) Age Conundrum: Contemporaneous with Imbrian-Aged Host Lava Flows or Emplaced in the Copernican?</p> <p>Journal of Geophysical Research: Planets, 126, e2021JE006880. https://doi. org/10.1029/2021JE006880</p>
On the utilization of Disruptive Technologies for Municipality-wide Policy Making
<p>Disruptive technologies (DTs) aim to introduce innovative elements with the immediate goal of changing citizens' behavior and the optimization of certain processes in either the private or public sector. The sector, especially "e-governance" has adopted several DT-based systems and is open to exploring new technologies that could be potentially useful for the sector's administration. This paper proposes a DT-based framework for the early detection of misinformation, the identification and prioritization of emerging and significant issues that refer to the municipality, and the recommendation of counter-actions that could be applied for each identified issue. The proposed framework aims to be applied initially at the municipal level as this is a more limited community for the framework's study and evaluation. News feeds and information posted on social media platforms will be the key information included in the proposed framework, aiming at sensing the public opinion and extracting issues and topics that concern a municipality's citizens. Through social media platforms, users tend to post, among other, concerns or issues they face within the local community, and thus useful content can be extracted and used for the identification of emerging and significant issues. The main advantage of this framework is the utilization of information from different data sources and their processing in order to immediately inform the relevant authority about the most important issues, allowing them to act in a proactive way. The expected outcome of the proposed framework is to raise the citizens' awareness of the issues of the local society and restrict criminality and violence in the municipality.</p>
WaveMAP analysis of extracellular waveforms from monkey premotor cortex during decision-making
<p>Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current <em>in vivo</em> methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (<em>WaveMAP</em>) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply <em>WaveMAP</em> to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using <em>WaveMAP</em>, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. <em>WaveMAP</em> therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.</p>
Making sense of virus size and the tradeoffs shaping viral fitness
Viruses span an impressive size range, with genome length varying a thousandfold and virion volume nearly a millionfold. For cellular organisms the scaling of traits with size is a pervasive influence on ecological processes, but whether size plays a central role in viral ecology is unknown. Here we focus on viruses of aquatic unicellular organisms, which exhibit the greatest known range of virus size. We outline hypotheses within a quantitative framework, and analyze data where available, to consider how size affects the primary components of viral fitness. We argue that larger viruses have fewer offspring per infection and slower contact rates with host cells, but a larger genome tends to increase infection efficiency, broaden host range, and potentially increase attachment success and decrease decay rate. These countervailing selective pressures may explain why a breadth of sizes exist and even coexist when infecting the same host populations. Oligotrophic ecosystems may be enriched in "giant" viruses, because environments with resource-limited phagotrophs at low concentrations may select for broader host range, better control of host metabolism, lower decay rate, and a physical size that mimics bacterial prey. Finally, we describe where further research is needed to understand the ecology and evolution of viral size diversity.
Visual processing and collective motion-related decision-making in desert locusts
<p>Collectively moving groups of animals rely on decision-making of locally interacting individuals in order to maintain swarm cohesion. However, the complex and noisy visual environment poses a major challenge to the extraction and processing of relevant information. We addressed this challenge by studying swarming-related decision-making in desert locust nymphs. Controlled visual stimuli, in the form of random dot kinematograms, were presented to tethered locust nymphs in a trackball setup, while monitoring movement trajectory and walking parameters. In a complementary set of experiments, the neurophysiological basis of the observed behavioral responses was explored. Our results suggest that locusts utilize filtering and discrimination upon encountering multiple stimuli simultaneously. Specifically, we show that locusts are sensitive to differences in speed at the individual conspecific level, and to movement coherence at the group level, and may use these to filter out non-relevant stimuli. The locusts also discriminate and assign different weights to different stimuli, with an observed interactive effect of stimulus size, relative abundance, and motion direction. Our findings provide insights into the cognitive abilities of locusts in the domain of decision-making and visual-based collective motion and support locusts as a model for investigating sensory-motor integration and motion-related decision-making in the intricate swarm environment.</p>
Co-creation Decision Making Tool
<p>Should I use co-creation?</p> <p>Decision-making tool</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.