Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,186
datasets available to search
ShareScore release 0.9.0
Dataset results
3,186 results for “efficiency”
A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories
<p>Dataset presented in Figures 3-7, S1 and S3 in the recently submitted AGU paper "A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories".</p>
Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons"
<p>Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript.</p>
Database and model code for "Material efficiency and climate change mitigation of passenger vehicles"
<p>This record provides all data points and model code necessary to compute the results presented in P. Wolfram, Q. Tu, N. Heeren, S. Pauliuk, E. Hertwich (2020) "Material efficiency and climate change mitigation of passenger vehicles", published in Journal of Industrial Ecology. All data is described in section 2 of the manuscript. The code can be run in MATLAB. </p>
SpEDIT: A fast and efficient CRISPR/Cas9 method for fission yeast
<p>Underlying data and extended data for:</p> <p><em>SpEDIT</em>: A fast and efficient CRISPR/Cas9 method for fission yeast</p>
Efficient pathways performances
<p>The dataset contains values of design indicators for all the efficient pathways identified by the Decision Analytic Framework in the DAFNE Project.</p> <p><strong>Zambezi River Basin</strong> (file <em>zrb_efficient_pathways.txt</em>)</p> <ul> <li>time horizon: 2020-2060</li> <li>design indicators: <ul> <li><em>j_environment</em>: Environmental flow deficit</li> <li><em>j_hydropower</em>: Hydropower production deficit</li> <li><em>j_irrigation_deficit</em>: Normalized irrigation deficit</li> <li><em>j_cost</em>: Total discounted cost</li> </ul> </li> </ul> <p><strong>Omo-Turkana Basin</strong> (file <em>otb_efficient_pathways.zip</em>)</p> <ul> <li>time horizon: 2002-2016</li> <li>design indicators: <ul> <li><em>j_Env</em>: Environmental flow deficit</li> <li><em>j_Hyd: </em>Hydropower production</li> <li><em>j_Irr</em>: Normalized irrigation deficit for large scale irrigation district</li> <li><em>j_Rec</em>: Deficit with respect to the target flood requirement in the Omo Delta for recession agricolture</li> <li><em>j_Fish:</em> Deficit of Fish biomass production in the Lake Turkana with respect to natural condition</li> </ul> </li> <li>pathways are related to four different configuration and labelled progressive within each group: <ul> <li>P0: Baseline, with no infrastructure development</li> <li>P1: Koysha, Baseline + Koysha dam</li> <li>P2: Irrigation, Baseline + Irrigation development</li> <li>P3: Irrigation and Koysha, Baseline + Irrigation development + Koysha dam</li> </ul> </li> </ul> <p>Indicators formulation and details on the efficient pathways generation can be found on DAFNE project Deliverables D5.2 and D5.4.</p> <p>These dataset have been used to populate the Multi-Perspective-Visual-Analytics tool and to perform the screening exercise during the second NSL meeting in both case studies (see also DAFNE Deliverables D7.4).</p>
Supplementary Material for "Route Efficiency Assessment and Review of the Synthesis of β-Nucleosides via N-Glycosylation of Nucleobases"
<p>This is the external Supplementary Material for our publication "Route Efficiency Assessment and Review of the Synthesis of β-Nucleosides via <em>N</em>-Glycosylation of Nucleobases", which has been released as a preprint on <em>ChemRxiv </em>(https://doi.org/10.26434/chemrxiv.12753413.v1). The files in this record are additionally available from <em>ChemRxiv</em>.</p>
Dataset for EASY: Efficient Arbiter SYnthesis from Multi-threaded Code
<p>High-level synthesis (HLS) is an increasingly popular method for generating hardware from a description written in a software language like C/C++. Traditionally, HLS tools have operated on sequential code, however in recent years there has been a drive to synthesise multi-threaded code. In this context, a major challenge facing HLS tools is how to automatically partition memory among parallel threads to fully exploit the bandwidth available on an FPGA device and minimise memory contention. Existing partitioning approaches require inefficient arbitration circuitry to serialise accesses to each bank because they make conservative assumptions about which threads might access which memory banks. In this work, we design a static analysis that can prove certain memory banks are only accessed by certain threads and use this analysis to simplify or even remove the arbiters while preserving correctness. We show how this analysis can be implemented using the Microsoft Boogie verifier, a tool named EASY for automatic formal verification using an SMT solver. Our work supports arbitrary input code with any irregular memory access patterns and indirect array addressing forms. We implement our approach in LLVM, integrate it with the LegUp HLS tool, and show that for a set of typical application benchmarks we can achieve up to 87% (avg. 61%) area savings and up to 39% (avg. 23%) improvement in execution time, with little additional compilation time relative to a long time in hardware synthesis.</p> <p>This repository includes all the measured results for this work.</p>
Data set for the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD"
<p>Data set to accompany the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD" written by B.A. Wilson (Swansea University) and P.D. Ledger (Keele University)</p>
Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics
<p>This record includes the raw data associated with the Lawrence Berkeley National Laboratory (LBNL) report "Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics," which is published <a href="https://escholarship.org/uc/item/44t4c2v6">here</a>.</p> <p>Data were collected by Guidehouse under the objective of developing current and projected performance, cost, and lifetime characteristics for residential and commercial building technologies and equipment with the potential to provide grid services. The list of technologies was developed based on data gathered from the U.S. Department of Energy (DOE) Grid Interactive Efficient Buildings (<a href="https://www.energy.gov/eere/buildings/grid-interactive-efficient-buildings">GEB</a>) Technical Report Series, ENERGY STAR Connected Certified Products, and input from researchers at the U.S. national laboratories. For each technology, characteristics are provided for a typical case and a connected or grid-interactive case. Where data are available, current DOE appliance standard levels are given. Definitions vary by technology and are provided with each data table. Current data is provided for 2020 and projections are available for 2030, 2040, and 2050. </p> <p><strong>Note</strong>: enabling technologies for grid-interactive efficient buildings such as smart meters and distributed energy management software were out of scope for this report. Non-building technologies such as EV chargers and PV inverters were also out of scope for this report.</p>
Sodium [18F]Fluoride PET Can Efficiently Monitor In Vivo Atherosclerotic Plaque Calcification Progression and Treatment
<p>Dataset for the research article entitled "Sodium [<sup>18</sup>F]Fluoride PET Can Efficiently Monitor <em>In Vivo </em>Atherosclerotic Plaque Calcification Progression and Treatment" published in the MDPI journal Cells.</p>
Improving Network Efficiency with Simplemux
<p>This dataset contains the open data related to the research paper:</p> <p>Jose Saldana, Ignacio Forcen, Julian Fernandez-Navajas, Jose Ruiz-Mas, "Improving Network Efficiency with Simplemux,'' IEEE CIT 2015, International Conference on Computer and Information Technology, 26-28 October 2015 in Liverpool, UK.</p> <p>This work has been partially financed by the EU H2020 Wi-5 project (Grant Agreement no: 644262), and European Social Fund in collaboration with the Government of Aragon.</p> <p><strong><em>Paper Abstract</em></strong>—The high amount of small packets currently transported by IP networks results in a high overhead, caused by the significant header-to-payload ratio of these packets. In addition, the MAC layer of wireless technologies makes a non-optimal use of airtime when packets are small. Small packets are also costly in terms of processing capacity. This paper presents Simplemux, a protocol able to multiplex a number of packets sharing a common network path, thus increasing efficiency when small packets are transported. It can be useful in constrained scenarios where resources are scarce, as community wireless networks or IoT. Simplemux can be seen as an alternative to Layer-2 optimization, already available in 802.11 networks. The design of Simplemux is presented, and its efficiency improvement is analyzed. An implementation is used to carry out some tests with real traffic, showing significant improvements: 46% of the bandwidth can be saved when compressing voice traffic; the reduction in terms of packets per second in an Internet trace can be up to 50%. In wireless networks, packet grouping results in a significantly improved use of air time.</p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
Precipitation Efficiency
<p>This dataset contains processed precipitation efficiency data derived from six global storm-resolving models. The calculation of the precipitation efficiency index follows the methodology outlined in Li et al. (2002a)</p>
Efficient assays to quantify the life history traits of algal viruses
<p>Data and analysis files accompanying the paper 'Efficient assays to quantify the life history traits of algal viruses' (Lievens et al. 2023, Applied & Environmental Microbiology). Includes data and code for the modified one-step growth (mOSG) assay, modified survival (mS) assay, and comparison of the two assays.</p> <p>The only difference with the previous version of this package (doi 10.5281/zenodo.6573770) is that two typos were corrected in the file "mOSG & mS comparison.rmd" (lines 172 & 182; corrections noted in the script).</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>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry
<p>The data supporting the findings in the manuscript "Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry" are available here.</p>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</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.