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50 results for “Fundamental Data”
Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions—data release
<p>This dataset contains the raw data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2202.05516">Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions</a>. </p> <p>Included are:</p> <ul> <li>The raw log output from the configuration generation, correlation function calculation, and Dirac eigenvalue computation, as well as metadata describing the ensembles used for the mass spectrum calculation, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `data.h5`.</li> <li>The fit parameters used to compute the spectrum, including the thermalisation length, and the plateau start and end points, in `fit_params.zip`.</li> <li>The data underlying tables 2–6 of the publication above, in CSV format.</li> </ul> <p>More details can be found in the file README.md.</p> <p>Version history:</p> <ul> <li>v1.1: Replace out_corr_48x24x24x24b6.5mas-1.01mf-0.71 due to a mistake where the wrong version of the measurement code was used.</li> <li>v1.0: Initial release</li> </ul>
Supporting data for "Fundamental limitations of cavity-assisted atom interferometry"
<p>Supporting data with code to generate Fig. 2 Cavity-induced deformation of a Gaussian input. Publication: DOI:https://doi.org/10.1103/PhysRevA.96.053820</p> <p>arXiv:1710.02448</p> <p>This dataset contains a zip file with raw data sets of all relevant measurements to plot figure 2.</p> <p>Figure 2. Envelope functions of the intracavity field for a 1 m cavity injected with<br> a 1μs pulse for different cavity finesses. All areas are normalized<br> to the input pulse area for comparison. When the pulse duration is<br> comparable to the photon lifetime of the cavity, its envelope function<br> is elongated. Inset: Envelopes without normalization.</p> <p> </p> <p>Further data and information are available from Miguel Dovale <mdovale@star.sr.bham.ac.uk> at reasonable request.</p> <p>School of Physics and Astronomy and Institute of Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom</p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </p>
Data for: A fundamental tradeoff among resilience, resistance, efficiency, and redundancy in tidal wetlands
<p>We filtered the raw NASA-MODIS (MOD13Q1) Enhanced Vegetation Index (EVI) dataset to only inlcude pixels with high tidal wetland class purity and Quality Assurance (QA) reliability scores. We filtered 782,693 tidal wetland pixels with coverage spanning the entire contiguous United States to only include those with greater than 90% tidal wetland class purity. We then further filtered these pixels to only include those where data dropouts in the EVI or QA layer occured fewer than 10% of the time. In the end, we used 145,871 pixels in our analysis. Tidal wetland GPP was calculated by pixel for the dates 3/5/2000 to 12/2/2020 at 16-day intervals using the algorithms published in: </p> <p>R. A. Feagin, I. Forbrich, T.P. Huff, J.G. Barr, J. Ruiz-plancarte, J.D Fuentes, R.G. Najjar, R. Vargas, A. Vazquez-lule, L. Windham-Myers, K. Kroeger, E.J. Ward, G.W. Moore, M. Leclerc, K.W. Krauss, C.L. Stagg, M. Alber, S.H. Knox, K.V.R. Schafer, T.S., Bianchi, J.A. Hutchings, H.B. Nahrawi, A. Noormets, B. Mitra, A. Jaimes, A.L. Hinson, B. Bergamaschi, J. King, and G. Miao., Tidal wetland gross primary production across the continental United States, 2000–2019. Global Biogeochemical Cycles 34, e2019GB006349 (2020).</p> <p>The file named "SWR_90percentFinal.csv" contains the filtered SWR database used to calculate GPP. File named "temp_90percentFinal_rounded.csv" contains the filtered air temperature database used to calculate GPP. The file named "EVI(gapped_filled)_90percentFinal.csv" contains the final gap-filled EVI time series database used to calculate GPP. File named "QA_90%Final.csv" contains the filtered quality assurance (QA) values. Dates in the EVI database where QA = 3 were determined to be of poor quality, removed from the database, and replaced with "NA". Single NA gaps in the EVI database were gap-filled by taking the mean of the dates flanking the NA gap. File named "myGPP(gap_filled)_FINAL.csv" contains the calculated GPP estimates used throughout the study analysis. </p> <p>File named "rawEVI.csv" contains the raw EVI database prior to filtering and gap-filling. File named "rawSWR.csv" contains the raw SWR database prior to filtering. File named "rawAirTemp.csv" contains the air temperature database prior to filtering. File named "rawQA.csv" contains the QA layer of the MOD13 satellite product prior to filtering. These files contain the raw data for all 782,693 tidal wetland pixel locations. These raw files can also be accessed at daac.ornl.gov. </p>
Data and code for: The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)
<p>Data and R code for <br>The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)</p>
Data and code from: Predicting the fundamental thermal niche of ectotherms
<p>Climate warming is predicted to increase mean temperatures and thermal extremes on a global scale. Because their body temperature depends on the environmental temperature, ectotherms bear the full brunt of climate warming. Predicting the impact of climate warming on ectotherm diversity and distributions requires a framework that can translate temperature effects on ectotherm life history traits into population- and community-level outcomes. Here we present a mechanistic theoretical framework that can predict the fundamental thermal niche and climate envelope of ectotherm species based on how temperature affects the underlying life history traits. The advantage of this framework is two-fold. First, it can translate temperature effects on the phenotypic traits of individual organisms to population-level patterns observed in nature. Second, it can predict thermal niches and climate envelopes based solely on trait response data and hence completely independently of any population-level information. We find that the temperature at which the intrinsic growth rate is maximized exceeds the temperature at which abundance is maximized under density-dependent growth. As a result, the temperature at which a species will increase the fastest when rare is lower than the temperature at which it will recover from a perturbation the fastest when abundant. We test model predictions using data from a native-invasive interaction to identify the temperatures at which the invader can most easily invade the native's habitat, and the native species is most likely to resist the invader. The framework is sufficiently mechanistic to yield reliable predictions for individual species, and sufficiently broad to apply across a range of ectothermic taxa. This ability to predict the thermal niche before a species encounters a new thermal environment is essential to mitigating some of the major effects of climate change on ectotherm populations around the globe.</p>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
The Right to Data Protection Ensuring the Autonomous Exercise of the Individual's Other Fundamental Rights
<p>The graphic illustrates the function of the fundamental right to data protection under Art. 8 ECFR protecting individuals against the risks caused by the processing of personal data against his or her other fundamental rights to privacy, freedom and non-discrimination.</p>
Data and code for figures in "Two-tone optomechanical instability and its fundamental implications for backaction-evading measurements"
<p>Here we prepare the data and process scripts to reconstruct figure 4 of the paper (Two-tone optomechanical instability and its fundamental implications for backaction-evading measurements). The folder contains several subfolders and files:</p> <p>“Raw data”: In this folder, you can find the raw data recorded by measurement devices during the experiment. It follows the hierarchical structure. We have three pairs of folders corresponding to three cooperativities (3.5, 7, 14). One folder of each pair contains raw data files in text format (.dat) and the other one contains plots and a Numpy dictionary of the extracted parameter for each cooperativity (superdict.npy). If you need to redo the extraction process from the raw data you can simply run “181031_CXX_Final_NOQT_BAE_2D_post_processeing.py” (XX: 3.5 or 7 or 14) python code to rewrite superdict.npy files and replot all plots in the Raw data folder.<br> “NRBcodes”: A side package for the circle fit (Lorentzian fitting) in the complex plane.<br> “Dicts”: A folder containing Numpy dictionaries needed for the final plot. “superdictXX.npy” are copies of Numpy dictionaries in the Raw data folder. “powers_XX.npy” and “Ds_nor_XX.npy” are Numpy dictionaries needed for the theory plots. You can reproduce them by the uncommenting first part of the “Final_plot.py” and correcting the corresponding cooperativity.<br> “getdata.py” and “postprocessing_libs.py”: Side packages help to read the raw data files.<br> <br> All post-processes are done on the raw data and you just need to run the “Final_plot.py” to reproduce the plot. If you want to access the post-processed data you can simply read “superdictXX.npy” in the Dicts folder. <br> Please do not hesitate to contact us in case of any questions. <br> amir.youssefi@epfl.ch</p>
Highly consistent brightness temperature fundamental climate data record from SSM/I and SSMIS
<p>The highly consistent brightness temperature (TB) fundamental climate data record (FCDR) comprises intercalibrated TBs from SSM/I on F11 and F13, and SSMIS on board F17. It covers the time period from December 1991 to December 2021. It provides homogenized and intercalibrated TBs in a user-friendly data format (HDF5). SSM/I and SSMIS data are used for various applications, such as analyses of the hydrological cycle. The improved homogenization and inter-calibration procedure ensure the long-term stability of the FCDR for climate related applications. <br> This data files contain daily TBs data on 1°×1° grid-level of satellite F11, F13 and F17 (Level 2A).<br> It is worth noting that the original sensor TB data are provided by Level-1C dataset. The Level-1C data record is complemented with scan status, quality flags, sun glint angles, and earth incidence angles.</p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
<p><span>Tropical rainforests around the world are rapidly being converted into cash-crop agricultural systems. The associated massive losses of plant and animal species lead to changes in arthropod food webs and the energy fluxes therein. These changes are poorly understood, in particular in the extremely biodiverse canopies of tropical ecosystems. Using canopy fogging followed by stable isotope and energy flux analyses, we show that land-use conversion from rainforest to rubber and oil palm plantations not only causes a drastic reduction in energy fluxes of up to 75% but also shifts fluxes among trophic groups. While rainforests featured high levels of both herbivory and algae-microbiology, and a balanced ratio of herbivory to predation, relative fluxes were shifted towards predation in rubber and towards herbivory in oil palm plantations, indicating profound shifts in ecosystem functioning. Our results highlight that the ongoing loss of animal biodiversity and biomass in tropical canopies degrades animal-driven functions and restructures canopy food webs.</span></p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
Open the record for dataset details and reuse information.
Data and code from: Predicting the fundamental thermal niche of ectotherms
Open the record for dataset details and reuse information.
Data from: The fundamental role of character coding in Bayesian morphological phylogenetics
<p>Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are four nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many character states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small.</p>
Hadron Shower Simulation Data for Generative Models in Fundamental Physics
<p>Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files:</p> <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> <p>Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.</p>
Original data for publication: Fundamental Loading-Curve Characteristics of the Persistent Phosphor SrAl2O4:Eu2+,Dy3+,B3+: The Effect of Temperature and Excitation Density
<p>Data for the paper:</p> <p>Teresa Delgado, Nando Gartmann, Bernhard Walfort, Fabio LaMattina, Markus Pollnau, Arnulf Rosspeintner, Jafar Afshani, Jacob Olchowka, Hans Hagemann<br> Fundamental Loading-Curve Characteristics of the Persistent Phosphor SrAl2O4:Eu2+,Dy3+,B3+: The Effect of Temperature and Excitation Density<br> Advanced Photonics Research, 2022, 3, 2100179, https://doi.org/10.1002/adpr.202100179</p>
Fundamental mode Rayleigh wave group velocity dispersion data
<p>Fundamental-mode Rayleigh-wave group-velocity dispersion curves have been computed from 14706 regional waveforms (ray-paths) sampling India, Himalaya, Tibet and surrounding regions. These have been combined through ray-based travel-time tomography to compute 2D group-velocity maps at periods between 10 and 120~s. These ray-paths sample the region with unprecedented density and uniformity. The 2D maps have a lateral resolution of 3\deg x3\deg, comparable to the regional geology. The dispersion curves, at each 1\deg\ spaced node-points, are inverted to obtain a 3D isotropic shear-wave velocity structure.</p>
FAIRmat Tutorial 11: Research data management, from fundamentals to implementation
<p>Scientific data are the key outcome of research activities at universities, research institutes, and industrial R&D centers. With the recent surge in research data production driven by high-throughput techniques, and the digitalization of scientific methods, data management has become more critical than ever. Moreover, multidisciplinary collaborative research requires the seamless exchange of research data between team members and different laboratories.</p> <p>To ensure that these vast amounts of diverse research data are handled effectively and result in valuable human knowledge and discoveries, it is imperative that proper data management practices are implemented. This ensures that data are produced in a high-quality, reproducible, and well-documented manner so that they can be machine-actionable and reusable in the future. </p> <p>Research Data Management (RDM) refers to the practices used in handling scientific data throughout their lifecycle, from collection to reuse.</p> <p>In this interactive tutorial, we will explain the different stages of the research data lifecycle and identify the best practices for each stage. We will explore the FAIR data principles —Findable, Accessible, Interoperable, and Reusable— and provide insights into their implementation during the research process. In addition, we will introduce the concept of data management plans, which define the data management process during research projects, along with practical tips on the various components and how to comply with funder requirements. </p> <p>The tutorial is divided into two sessions:</p> <ol> <li>Basic concepts and definitions of FAIR data and RDM</li> <li>Practical tips and examples for applying best practices in RDM</li> </ol> <p>After completing this tutorial, you will be able to: </p> <ul> <li>Describe the FAIR principles and understand the different aspects and implementations of FAIR data.</li> <li>Describe and justify measures for good RDM at different stages of the data lifecycle.</li> <li>Understand and explain the concepts of persistent identifiers (PID), metadata, data storage systems, etc. </li> <li>List and understand the contents of the components of a DMP according to the requirements of different funders in Europe. </li> <li>Identify the tools available to create a DMP.</li> </ul>
Data from: Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves
<p>This folder contains data from</p> <p>Shimizu, K. (2024), Fundamental properties of adjoint model and adjoint sensitivity under fully-nonlinear hydrostatic internal gravity waves, Journal of Geophysical Research: Oceans, 129, e2023JC020577. https://doi.org/10.1029/2023JC020577</p> <p>Its contents are briefly described in ReadMe.txt.</p>
Supplementary Data for Paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols"
<p>This is the result data of the user study described in the paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols".</p> <p>Author information and further metadata will be added after anonymous peer review.</p> <p>27 participants, 4 displays, 6 tasks. Note that the data contains the station ID (A-D), which maps to Displays 1-4 as described in the paper: A - D2; B - D4; C - D1; D - D3;</p> <p>File description:</p> <ul> <li>all_aggregated_users.csv: Thresholds for each task and station, for each participant. One row per participant, with fields for each station/task combination (27 rows).</li> <li>all_aggregated_thresholds.csv: Thresholds for each participant, task and station. One row per threshold value, thresholds for task #2 for participants #1-3 have been discarded, due to an error in the experiment configuration (see paper). (27 x 4 x 6 - 3 x 4 = 636 rows)</li> </ul>
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.