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3,481 results for “data set”
Data set of resilience indices to drought and prescribed burning of Pinus nigra ssp. salzmannii and P. sylvestris L. trees
<p>Data on resilience and resistance indices to drought and burning inferred from tree ring width and carbon 13 isotopes for burned and unburned <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris </em>trees<em>.</em>The dataset contains two files:</p> <p>TreeGrowth.txt: Data on resilience and resistance indices to drought and prescribed burning as well as the ratio of latewood to earlywood. Indices are inferred from total tree-ring, earlywood, and latewood widths. Included variables:</p> <ul> <li> Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li> Idplot (factor): code to identify uniquely burned plots. Four levels: Miravé-Fall (1), Mirave-Spring (2), Lloreda-Fall (3) and Lloreda-Spring (4).</li> <li>Treatment (factor): whether the plot was burned or not. Two levels: control or left unburned (C) or burned (B)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall burn (F) or spring burn (S)</li> <li>Sp (factor): species. Two levels: <em>Pinus sylvestris</em> (ps) or<em> Pinus nigra </em>(pn)</li> <li>TreeCode (numeric): code to identify trees in the field</li> <li>dbh (numeric): diameter at breast height (cm)</li> <li>c12 (numeric): competition index before burning</li> <li>rci15 (numeric): release from tree competition 2 years post-burning calculated as the difference between pre (CI12) and post-burning competition (CI15) indices relative to pre-burning levels </li> <li>bchmin (numeric): minimum bole scorch height (cm)</li> <li>bchmax (numeric): maximum bole scorch height (cm)</li> <li>whiteAshes (numeric): white ashes after burning (%, in 1m radius from tree center)</li> <li>Resistance (numeric): Average basal area increment (BAI) during the stress period (drought 2012 and prescribed burning 2013) divided by the average BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR (numeric): Average total tree ring BAI of the two years after the stress period (2014 and 2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR14 (numeric): Total tree ring BAI of the first year after the stress period (2014) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR15 (numeric): Total tree ring BAI of the second year after the stress period (2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW (numeric): Average earlywood BAI of the two years after the stress period (2014 and 2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW14 (numeric): Earlywood BAI of the first year after the stress period (2014) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW15 (numeric): Earlywood BAI of the second year after the stress period (2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW (numeric): Average latewood BAI of the two years after the stress period (2014 and 2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW14 (numeric): Latewood BAI of the first year after the stress period (2014) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW15 (numeric): Latewood BAI of the second year after the stress period (2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>preLWEW (numeric): mean pre-stress ratio of latewood to earlywood calculated for the period 2006-2011</li> <li>difLWEW (numeric): mean post-stress latewood:earlywood (2014-2015) minus mean pre-stress latewood:earlywood (2006-2011)</li> </ul> <p>d13c.txt: Early and late-wood carbon 13 isotope from 2011 to 2015 for <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris</em> burned in spring and fall in year 2013 at two sites. </p> <ul> <li>Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li>Sp (factor): species. Two levels: Pinus sylvestris (ps) or Pinus nigra (pn)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall (F) or spring (S)</li> <li>TreeCode (factor): code to identify trees in the field."Pool" means that a pool of 5 individuals was used to determine carbon 13 isotope.</li> <li>SeasonalGrowth (factor): seasonal wood growth. Two levels: earlywood (E) and latewood (L).</li> <li>year (numeric): calendar year.</li> <li>d13c (numeric): carbon 13 isotope<br> </li> </ul>
NYC Real-Time Traffic Speed Data Set (2015-2019)
This repository is an urban traffic speed data set (2015-2019) collected in New York City. 中文描述: 本数据集为纽约城市路网车速数据集, 采集时间为2015年4月至2019年3月, 数据文件是以csv格式保存, csv文件名为【年份 + 月份】.
Gold standard taxonomic profile of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<p><strong>DataURL: </strong> https://data.cami-challenge.org/participate</p>
Testing ritual knot tracing for cognitive priming effects rules out analytic analogy: Core Data Sets
<p>Core data sets analyzed for Studies 1 and 2 in "Testing ritual knot tracing for cognitive priming effects rules out analytic analogy".</p> <p>Note: In keeping with Ryerson University Research Ethics Board protocol #REB 2017-065, data sets are fully anonymized, revealing coded values only and removing all personal information and metadata peripheral to the main study (including reported age, gender, language proficiency, and language usage coding).</p> <p>See main paper and supporting materials for discussion of measures, parameters, conditions, and variables.</p> <p>Corresponding author contact: jpelkey@ryerson.ca</p>
Synthetic Data Set for Uplift Modeling
<p>This dataset is designed and simulated for evaluating uplift modeling and feature selection methods. The main feature of this dataset is that it generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</p> <p>This dataset consists of 100 trials (replicates with different random seeds), each trial with 10,000 samples and 36 features. The outcome variable is binary, that makes this dataset for classification problem. The samples are equally split for control and treatment group (5,000 samples in each group in each trial).</p> <p>The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To model the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine.</p> <p>In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features.</p> <p>Column names:</p> <ul> <li>Trial ID: 'trial_id'</li> <li>Experiment group label: 'treatment_group_key'</li> <li>Outcome variable (classification label): 'conversion'</li> <li>Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']</li> <li>True underlying control conversion probability: 'control_conversion_prob'</li> <li>True underlying treatment conversion probability: 'treatment1_conversion_prob'</li> <li>True treatment effect: 'treatment1_true_effect'</li> </ul>
Input data set for Professor/Apprentice
<p>Example MC data set of rivet runs and yoda output. This is educational material to be used as input to professor/apprentice.</p>
Data sets for "Oxygen vacancy substitution linked to ferric iron in bridgmanite at 27 GPa" by Fei et al.
<p>This is the EPMA, XRD, and Mossbauer datasets for the article "Oxygen vacancy substitution linked to ferric iron in bridgmanite at 27 GPa" by Fei et al.</p>
International artificial intelligence research progress and visual analysis of hot spots——data set
<p>International artificial intelligence research progress and visual analysis of hot spots——data set</p>
Keep it real: Selecting realistic sets of urban green space indicators - Supplementary data
<p>This excel sheet contains, for each of the four studied cities, the conceptual framework that is described in the paper "Keep it real: Selecting realistic sets of urban green space indicators".</p> <p>Each of the cities first listed all possible indicators that they could think of. Next, they indicated how each of those indicators relates to each of the KPI; i.e. whether the indicator can not at all (0), somewhat (1) or perfectly measure (2) the KPI. Lastly, the city authorities indicated whether the indicators are implemented of not, and scored some measures of indicator quality (relevance, feasibility, clarity, and credibility)</p>
'Learning the production cross sections of the Inert Doublet Model' training data set.
<p>Training data set used in the ''Learning the production cross sections of the Inert Doublet Model'' subproject, made of 50000 samples with 5 input values (MH0, MA0, MHC, lam2, lamL) and 8 target values (xsec_3535_13TeV, xsec_3636_13TeV, xsec_3737_13TeV, xsec_3537_13TeV, xsec_3637_13TeV, xsec_3735_13TeV, xsec_3736_13TeV, xsec_3536_13TeV) from a parameter space of the Inert Doublet Model chosen as: 50< MH0, MA0, MHC<3000GeV;−2π < lam2,lamL<2π. The cross sections were computed at leading order using MADGRAPH2.6.4 and the IDM UFO implementation from the FeynRules data base.</p> <p> </p> <p> </p>
S-SAD data set used for solving the structure of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id 6YP6
<p>S-SAD data set used for solving the structure of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B</p> <p>Data were measured at Diamond I02 on February 1, 2014</p> <p>PDB id is 6YP6</p> <p> </p>
Data set for "Determining the minimum energy requirement of an LNG process: New insights into the impact of the vapour liquid equilibrium"
<p>Accompanying information for Xuan et al, 2020, "Determining the minimum energy requirement of an LNG process: New insights into the impact of the vapour liquid equilibrium", Energy, Paper number 117785, <a href="https://doi.org/10.1016/j.energy.2020.117785">https://doi.org/10.1016/j.energy.2020.117785</a></p>
Data Set Primary Studies for Agent-Based Software Testing: A Systematic Mapping Study
<p>This document contains the final set of primary studies used in our systematic mapping study that has been conducted with a set of 41 selected papers regarding agent-based systems in software testing. </p>
Data set for "Improving double-ended transition state searches for soft-matter systems"
<p>Data set for "Improving double-ended transition state searches for soft-matter systems", JCP, 2020</p>
Data sets for "Chemical reaction between iron and a limited water supply under pressure: implications for water behavior at the core-mantle boundary" by Nishi et al.
<p>This is the XRD datasets for the article "Chemical reaction between iron and a limited water supply under pressure: implications for water behavior at the core-mantle boundary" by Nishi et al.</p>
Impactos da COVID-19 na produtividade de desenvolvedores de software brasileiros - Data set
<p>Impactos da COVID-19 na produtividade de desenvolvedores de software brasileiros - data set</p>
Carbon dioxide and methane exchange of a patterned subarctic fen during two contrasting growing seasons [Data set]
<p>The data set contains carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes on ecosystem and plant community level, and ancilliary meteorological and environmental data, measured at Kaamanen fen in Northern Finland (N69°8.435', E27°16.189', 155 m a.s.l.), during 2017 - 2018. Additionally, the data set contains soil data from the fen, forest and lake ecosystems in the catchment, measured by Juha Mikola.</p> <p>C_fluxes_Heiskanen_et_al.csv includes ecosystem scale quality screened, u* filtered and gap-filled eddy covariance flux data and plant community scale flux data modelled from manual flux chamber measurements.</p> <p>environmental_data1_Heiskanen_et_al.csv and environmental_data2_Heiskanen_et_al.csv include ancillary meteorological and environmental data.</p> <p>LAI_data_2017_Heiskanen_et_al.csv and LAI_data_2018_Heiskanen_et_al.csv include leaf area index data.</p> <p>UEF_data_with_LCTs.xlsx includes soil data from peatlands, mineral soils and lake sediments, by Juha Mikola.</p>
Global Data Set on Spread of COVID-19 and Ambient Temperature
<p>The Novel Coronavirus (COVID-19) daily data of confirmed cases for affected countries and provinces of China reported between 31st December 2019 and 31st May 2020. The data was collected from the European Centre for Disease Prevention and Control (ECDC), and John Hopkin CSSA. </p> <p>The monthly mean temperature of February to May 2020 of capital cities for the various nations.</p>
Paper Data set
<p>The zip file contains four folders:</p> <p>-ChessData contains all the data used for the Chess case study including the Traces.json file that represents all the requirement-to-method traces collected.</p> <p>-GanttData contains all the data used for the Gantt case study including the Traces.json file that represents all the requirement-to-method traces collected.</p> <p>-iTrustData contains all the data used for the iTrust case study including the Traces.json file that represents all the requirement-to-method traces collected.</p> <p>-JHotDrawData contains all the data used for the JHotDraw case study including the Traces.json file that represents all the requirement-to-method traces collected.</p>
Data for "Applying Heat and Humidity using Stove Boiled Water for Decontamination of N95 Respirators in Low Resource Settings"
<p>Raw Data</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.