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1,157 results for “Limited data”
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
Experimental data for Quantum noise limited microwave amplification using a graphene Josephson junction
<p>This dataset was used in our study of "Quantum noise limited microwave amplification using a graphene Josephson junction".</p>
Replication Materials for Disclosure Limitation and Confidentality Protection in Linked Data
<p>These are the data and derived figures as used in the chapter by Abowd, Schmutte, and Vilhuber, "Disclosure Limitation and Confidentiality Protection in Linked Data"</p>
Data from: The thermal limits of native plant species in California Coastal Sage Scrub
<p>Field and laboratory data for Goldsmith et al. (<em>In Review</em>) entitled, "The thermal limits of native plant species in California Coastal Sage Scrub." Four data files are included: </p> <p><strong><em>Goldsmithetal_PlantFunctionalTraitMetaData-18July24.xlsx </em></strong>-Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "<em>Goldsmithetal_PlantFunctionalTraitData-18July24.csv." </em></p> <p><em><strong>Goldsmithetal_PlantFunctionalTraitData-18July24.csv </strong>- </em>Provides raw data for field and lab observations of plant functional traits as described in the methods section of this data record. </p> <p><em><strong>Goldsmithetal_PlantFvFmLabData-29March24.csv </strong>- </em>Provides raw data for experimental lab observations of leaf fv/fm following experimental heat treatments as described in the methods section of this data record. <em><br></em></p> <p><em><strong>Goldsmithetal_PlantFvFmLabMetaData-2Aug23.xlsx</strong> - </em>Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "Goldsmithetal_PlantFvFmLabData-29March24.csv." </p> <p> </p> <p>Contact Greg Goldsmith (goldsmith at chapman dot edu) for additional information. </p>
Replication Data for: "Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts"
<p>These datasets contain all the raw data needed to replicate the results from our paper <em>Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts</em> published in eLife - <a href="https://doi.org/10.7554/eLife.81080">https://doi.org/10.7554/eLife.81080</a></p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022
This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
Data and Code in support of Nitrogen and phosphorus co-limitation of forest growth in northern hardwood forests; Blumenthal et al. 2025
The Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) project studies N and P acquisition and limitation of forest productivity through a series of nutrient manipulations in northern hardwood forests. This data set is published in support of a manuscript titled "Nitrogen and phosphorus co-limitation of forest growth in northern hardwood forests" and includes data and code used in the analysis. The primary diameter breast height dataset can be found in: Fisk, M.C., R.D. Yanai, and T.J. Fahey. 2025. Tree DBH response to nitrogen and phosphorus fertilization in the MELNHE study, Hubbard Brook Experimental Forest, Bartlett Experimental Forest, and Jeffers Brook ver 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/fb8f8d5b903627bee9ad6aa4c32f2289. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
CEPHIA Evaluation Panel data for Maxim and Sedia Limiting Antigen Avidity assays
<p>CEPHIA Evaluation Panel dataset containing final ODn values for each assay. The first file contains results on all standard panel samples, and the second contains results on blinded control samples tested in replicate.</p>
Data from the parametric analysis of masonry panels with limit analysis
<p>For each one of the simulations performed from the parametric analysis of masonry panels with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapse time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
Epitaxial HfTe2 Dirac semimetal in the 2D limit (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Epitaxial HfTe<sub>2</sub> Dirac semimetal in the 2D limit</em>" by P. Tsipas et al., APL Materials <strong>9</strong>, 101103 (2021); <a href="https://doi.org/10.1063/5.0065839">https://doi.org/10.1063/5.0065839</a></p> <p>An Open Access version of the paper can be found here: <a href="https://doi.org/10.1063/5.0065839">https://doi.org/10.1063/5.0065839</a></p>
Data for study "Realisation of Paris Agreement pledges may limit warming just below 2°C"
<p>This is the data repository with country-level data and individual scenario data for the study “Realisation of Paris Agreement pledges may limit warming just below 2°C”. Please find below a description of the different data sources available. </p> <p><strong>Emissions Scenario Data (scenarios_12Nov2021a_CR.csv). </strong>The complete set of infilled and extended emissions for each scenario presented in the study. The emissions timeseries span from 2015 to 2100 for 52 emissions species required to run MAGICC7.</p> <p><strong>Scenario Statistics Table (stats_12Nov2021a_CR_short.csv).</strong> Summary statistics for the scenarios used in the sensitivity study. Statistics available include Exceedance probabilities, peak warming, year of peak warming and warming in 2100.</p> <p><strong>Supplementary Table S1 (supplementary_table-s1.csv) - </strong>Estimate of total GHG emissions (excl. LULUCF) in 1990, 2010, 2019 and for 2025 and 2030 under the NDCs. The ‘lower’ and ‘higher’ end of the range span the four cases of full implementation and ‘unconditional-only’ NDC quantification, with their respective ranges (e.g. Australia’s 26%-28% target). Historical data based on country-level data in NDCs and PRIMAP-hist CR. Aggregation of CO2, CH4, N2O, HFCs, PFCs, and SF6 emissions performed in GWP-100 AR6 metrics. Note that the common reporting guidelines under the Paris Agreement’s transparency framework currently chose GWP-100 AR5 metric as default (Decision 5/CMA.3, UNFCCC, 2021).</p> <p><strong>Supplementary Table S2 (supplementary_table-s2.csv) </strong>An estimate of potentially additional methane reductions if the Global Methane Pledge were implemented on a country-by-country level with a 30% reduction. Of the 104 countries signed up by 2 Nov 2018, 101 are here considered (with no sufficient methane data availability for Niue, Palau and Federal State of Micronesia).<strong> </strong></p> <p><strong>Scenario Timeseries (timeseries_12Nov2021a_CR.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for NDC scenarios presented in the study.<strong> </strong></p> <p><strong>IEA Scenario Timeseries (timeseries_IEA.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for the IEA scenarios presented in the study.</p> <p>Contact Jared Lewis <jared.lewis@climate-resource.com> for additional data about the target scenarios.</p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
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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)
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DANDI Archive for NWB datasets
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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.