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77 results for “Decentral”
Census of the Ecosystem of Decentralized Autonomous Organizations
<p>The dataset includes data from various Decentralized Autonomous Organizations (DAOs) platforms, namely Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. DAOs are a new form of self-governed online communities deployed in the blockchain. DAO members typically use <em>governance tokens</em> to participate in the DAO decision-making process, often through a voting system where members submit proposals and vote on them.</p> <p>The description of the methods used for the generation of data, for processing it and the quality-assurance procedures performed on the data can be found here:<br><a href="https://doi.org/10.1145/3589335.3651481">https://doi.org/10.1145/3589335.3651481</a></p> <ul> <li>Recommended citation for this dataset:<br>Peña-Calvin, A., Arroyo, J., Schwartz, A., & Hassan, S. (2024). Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations. Companion Proceedings of the ACM Web Conference, 13–17, 2024, Singapore, doi: <a href="https://doi.org/10.1145/3589335.3651481">https://doi.org/10.1145/3589335.3651481</a></li> </ul> <p>The dataset comprises three CSV files: deployments.csv, proposals.csv, and votes.csv, each containing essential information regarding DAOs deployments, their<br>proposals, and the corresponding votes.</p> <ul> <li>The file deployments.csv provides insights into the general aspects of DAO deployments, including the platform it is deployed in, the number of proposals, unique voters, votes cast, and estimated voting power.</li> <li>The proposals.csv file contains comprehensive information about all proposals associated with the deployments, including their date, the number of votes they received, and the total voting power voters employed on that proposal.</li> <li>In votes.csv, data regarding the votes cast for the deployment proposals is recorded. It includes the voter's blockchain address, the vote's weight in voting power, and the day it was cast.</li> </ul>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Evaluation Data of a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "Trusting Decentralized Web Data in a Solid-based Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>calculations.csv:</strong> All calculated numbers based on the raw data of the conducted empircal user study.</li> <li><strong>questions_translation.csv:</strong> A translation of all German questions asked in the survey to English, including a mapping of the question codes to the questions.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> </ul>
Extended Evaluation Data of TrADS: a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "TrADS: a Trust-Aware Decentralized Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> <li><strong>all.xlsx:</strong> An Excelfile containing all the following .CSV files as worksheets.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>group1_unfiltered.csv:</strong> All participants' data of group 1.</li> <li><strong>group2_unfiltered.csv:</strong> All participants' data of group 2.</li> <li><strong>group1.csv:</strong> All filtered participants' data of group 1, who correctly answered the control questions.</li> <li><strong>group1_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 1. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group1_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 1. Only including the question 5 about dimension importance.</li> <li><strong>group1_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 1.</li> <li><strong>group2.csv:</strong> All filtered participants' data of group 2, who correctly answered the control questions.</li> <li><strong>group2_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 2. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group2_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 2. Only including the question 5 about dimension importance.</li> <li><strong>group2_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 2.</li> <li><strong>participants.csv:</strong> General information about participants grouped by both groups and joint.</li> <li><strong>likert_questions.csv:</strong> Mean Values and Standard Deviations (Std) of all statements rated on a 5-point Likert scale. It includes Means and Stds for Group 1, Group 2, Group 1 + Group 2 concatinated, and the values of the first user study published in the previous paper on TrADS <a href="https://zenodo.org/records/10641724" target="_blank" rel="noopener">(also available in previous dataset on Zenodo)</a>.</li> </ul>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
Dataset: Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation
<p>Dataset resulting from the experiments conducted in "Decentralized Cross-Blockchain Asset Transfers with Transfer Confirmation"</p>
"Who Cares?": The Acceptance of Decentralized Wastewater Systems in Regions without Water Problems
<p>Transcripction of focus groups for the paper “Who Cares?”: The Acceptance of Decentralized Wastewater Systems in Regions without Water Problems. <a href="https://doi.org/10.3390/ijerph17239060">https://doi.org/10.3390/ijerph17239060</a> <em>Int. J. Environ. Res. Public Health</em> <strong>2020</strong>, <em>17</em>(23), 9060</p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20ARQUITECTOS.docx">TRANSCRIPCIÓN FOCUS GROUP ARQUITECTOS</a></p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20ECOLOGISTAS.docx">TRANSCRIPCIÓN FOCUS GROUP ECOLOGISTAS.docx </a></p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20POBLACI%C3%93N%20GENERAL%201.docx">TRANSCRIPCIÓN FOCUS GROUP POBLACIÓN GENERAL 1.docx </a><br> <br> <a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20POBLACI%C3%93N%20GENERAL%202.docx">TRANSCRIPCIÓN FOCUS GROUP POBLACIÓN GENERAL 2.docx </a><br> </p>
DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery
<p>DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery</p> <p>Science of The Total Environment, Volume 812, 15 March 2022, 151485</p> <p>10.1016/j.scitotenv.2021.151485</p> <p> </p>
Decentralized Applications Network Structure
<p>This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore. Each dApp's network structure is represented through a CSV which includes the following information:<br>File: The name of the Solidity file<br>Source_Contract: The name of the contract that calls the Target contract.<br>Target_Contract: The name of the contract that is called by the Source contract.<br>Source_Function: The name of the function that calls the Target function.<br>Target_Function: The name of the function called by the Source function.<br>Chain: The chain of function calls. </p>
Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material
<p>This repository holds supplementary material for research concerning the <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em></p> <p>which was published as a working paper as</p> <p>Czock, Berit, Cordelia Frings, and Fabian Arnold. <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em>. No. 2024-4. Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), 2024.</p> <p>and is available at</p> <p>https://www.ewi.uni-koeln.de/de/publikationen/cost-and-cost-distribution-of-policy-driven-investments-in-decentralized-heating-systems-in-residential-buildings-in-germany/</p> <p>This repository includes the following documents</p> <ol> <li>building list</li> <li>description of technical and economic assumptions</li> <li>detailed description of results</li> </ol> <p>Further material can be made available on request.</p> <p> </p> <p> </p> <div> <p>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material Creators Czock, Berit1 © 2024 by Berit Hanna Czock is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer"> CC BY 4.0 </a></p> </div>
An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments (video demonstration)
<p>An Attribute-Based Access Control model in RFID systems based on blockchain Decentralized Applications for healthcare environments.</p>
Feature Dataset of Centralized & Decentralized Communication Apps
<p>This dataset contains features for 44 centralized and decentralized communication applications. In total, 77 different features identified in these 44 applications.</p>
Formalized information from the study protocols of three decentralized studies on rare diseases within the CORD project
<p>The file contains formalized information from the study protocols of decentralized studies on rare diseases within the CORD project. This includes the diagnoses that are coded with ICD-10-GM. The following diseases or use cases are described:</p><ul><li>Cystic Fibrosis and Pregnancy or Delivery</li><li>Phenylketonuria, Comorbidities and Pregnancy or Delivery</li><li>Kawasaki and Pediatric Inflammatory Multisystem Syndrome (PIMS)</li></ul>
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
<p>The video illustrates simulation and experimental results of a team of unmanned aerial vehicles executing Linear Temporal Logic (LTL) tasks. More specifically, given a certain LTL task over predefined regions of interest, each agent derives a high-level plan that satisfies the given task. Then, it executes the plan using a continuous controller that is based on decentralized navigation functions, which also guarantee inter-agent collision avoidance. The video shows one simulation and two experimental scenarios.</p>
DIN: A Decentralized Inexact Newton Algorithm for Consensus Optimization: SW and Data
<p>This upload contains the main simulation code and related datasets used in the conference paper entitled <a href="https://ieeexplore.ieee.org/document/10278721" target="_blank" rel="nofollow noreferrer noopener">DIN: A Decentralized Inexact Newton Algorithm for Consensus Optimization</a>, which was presented at <a href="https://icc2023.ieee-icc.org/" target="_blank" rel="nofollow noreferrer noopener">IEEE ICC 2023</a>.</p>
Data for Paper: A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system
<p>Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.</p>
Recordings from the C. borealis Stomatogastric Nervous System at different temperatures in the decentralized condition
<p>As a courtesy please inform us if you are planning to work with these data (marder@brandeis.edu).</p> <p>These data are collected from the stomatogastric nervous system of the crab <em>Cancer borealis</em> in the decentralized condition (modulatory inputs cut) at different temperatures. For data collection methods please see Haddad & Marder, 2018 (DOI: <a href="https://doi.org/10.1016/j.neuron.2018.08.035">10.1016/j.neuron.2018.08.035</a>). These data are also among the larger data set described in Gorur-Shandilya et al, 2021 (https://www.biorxiv.org/content/10.1101/2021.07.06.451370v1.full.pdf). </p> <p>In data sets 845_082 and 845_078 there are two preparations recorded from in each file. Please see notes to clarify which channels belong to which preparations. Please see end for descriptions of nerve/neuron abbreviations. </p> <p>Data set 845_082_0044 (11°C) and 845_082_0064 (27°C):</p> <p> Preparation 1 = Channels 1 (lvn), 6 (lpn), 2 (pyn), 4 (pdn)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pdn), 13 (lgn)</p> <p>Data set 857_016_0049 (11°C) and 857_016_0069 (27°C):</p> <p> Preparation 1 = Channels 2 (LG), 7 (lvn), 4 (lpn), 9 (pyn), 8 (pdn), 15 (lgn), 6 (mvn), 11 (dgn)</p> <p>Data set 845_078_0027 (11°C) and 845_078_0040 (27°C)</p> <p> Preparation 1 = Channels 1 (lvn, upper), 6 (lpn), 4 (pyn), 2 (lvn lower)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pyn), 13 (pdn)</p> <p>lvn = lateral ventricular nerve, lpn = lateral pyloric nerve, pyn = pyloric nerve, pdn = pyloric dilator nerve, lgn = lateral gastric nerve, mvn = median ventricular nerve, dgn = dorsal gastric nerve, LG= lateral gastric neuron.</p>
Categorization of Decentralized Autonomous Organizations in the Aragon platform
<p>Dataset result of the research paper "A Categorization of Decentralized Autonomous Organizations: The Case of the Aragon Platform" published in <em>IEEE Transactions on Computational Social Systems</em>. The paper proposes an empirically grounded categorization of DAOs in terms of their operative domain, purpose, scope, voting processes, and use of crypto-tokens. The categorization was applied to 40 DAO communities hosted in the Aragon platform, analyzing 15 dimensions in each of them. Further details on the variables and the annotation process are described in the paper.</p> <p>Recommended citation for the article: </p> <p>Peña-Calvin, A., Saldivar, J., Arroyo, J., & Hassan, S. (2023). A Categorization of Decentralized Autonomous Organizations: The Case of the Aragon Platform. <em>IEEE Transactions on Computational Social Systems</em>, doi: 10.1109/TCSS.2023.3299254</p> <p>WHAT'S NEW IN THIS VERSION</p> <p>- Readme file. It includes detailed information on the content of the rest of the files.</p> <p>- Erratum corrected in file Dataset - Tokens.csv. Token NU was labelled as TOK-4 and now is labelled as TOK-3.</p>
The emergence of division of labor through decentralized social sanctioning
<p>Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specialization and division of labor. Unlike most other animals, humans learn by trial and error during their lives what role to take on. However, when some critical roles are more attractive than others, and individuals are self-interested, then there is a social dilemma: each individual would prefer others take on the critical but unremunerative roles so they may remain free to take one that pays better. But disaster occurs if all act thusly and a critical role goes unfilled. In such situations learning an optimum role distribution may not be possible. Consequently, a fundamental question is: how can division of labor emerge in groups of self-interested lifetime-learning individuals? Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles. Such social norms work by redistributing rewards within the population to disincentivize antisocial roles while incentivizing prosocial roles that do not intrinsically pay as well as others.</p>
Acute Respiratory Illness Surveillance (AcRIS) With Mobile Application in a Low-Interventional Decentralized Study.
ClinicalTrials.gov study NCT04748445. IPD Sharing: NO. Countries: 1. Publications: 2.
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.