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.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “product distribution”
Data set for the journal article: The Spatial Distribution of Cobalt Phthalocyanine and Copper Nanocubes Controls the Selectivity towards C2 Products in Tandem Electrocatalytic CO2 Reduction
<p>Data set for the journal article:</p> <p>The coupling of CO-generating molecular catalysts with copper electrodes in tandem schemes is a promising strategy to boost the formation of multi-carbon products in the electrocatalytic reduction of CO<sub>2</sub>. While the spatial distribution of the two components is important, this aspect remains underexplored, especially for molecular-based tandem systems. Herein, we address this knowledge gap by studying tandem catalysts comprising Co-phthalocyanine (CoPc) and Cu nanocubes (Cu<sub>cub</sub>). In particular, we identify the importance of the relative spatial distributions of the two components on the performance of the tandem catalyst by preparing CoPc-Cu<sub>cub</sub>/C, wherein the CoPc and Cu<sub>cub</sub> share an interface, and CoPc-C/Cu<sub>cub</sub>, wherein the CoPc is loaded first on carbon black (C) before mixing with the Cu<sub>cub</sub>. The electrocatalytic measurements of these two catalysts show that the faradaic efficiency towards C<sub>2 </sub>products almost doubles for the CoPc-Cu<sub>cub</sub>/C, whereas it decreases by half for the CoPc-C/Cu<sub>cub</sub>, compared to the Cu<sub>cub</sub>/C. Our results highlight the importance of a direct contact between the CO-generating molecular catalyst and the Cu to promote C-C coupling, which hints at a surface transport mechanism of the CO intermediate between the two components of the tandem catalyst instead of a transfer via CO diffusion in the electrolyte followed by re-adsorption.</p>
Data from: Range-wide distribution of genetic diversity in the North American tree Juglans cinerea: a product of range shifts, not ecological marginality or recent population decline
Open the record for dataset details and reuse information.
Labor Productivity Climate Impact Distributions
<p>Percentiles of labor productivity impacts due to climate change, organized by level of aggregation, climate scenario, and time period.</p> <p>Labor productivity impacts are estimated by applying future climate data to dose-response functions of absenteeism for low and high-sensitivity sectors.</p> <p>The uncertainty represented by these percentiles comes from a combination of statistical uncertainty in the dose-response functions, climate uncertainty across GCMs, and within-month weather realization, as sampled by Monte Carlo runs.</p> <p>The archive contains four folders: county_20a with county-level impacts, state_20yr with state-aggregated impacts, nca_20yr with NCA-region aggregated impacts, and national_20y with nationally aggregated impacts. Aggregation is weighted by jobs within each region.</p> <p>The files are labeled as follow:</p> <p>labor-<SENSITIVITY>-<RCP>-<YEAR><SUFFIX>.</p> <p>The SENSITIVITY can be high, for outdoor jobs, or low for indoor tasks. The RCP may be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5; the years are "2020", for average impacts during 2020 - 2039, "2040" for impacts 2040 - 2059, and "2080" for impacts 2080 - 2099. Files with the suffix 'b.csv' contain percent changes; those with the suffix '-absolute.csv' contain level changes in MT.</p> <p>The columns of these files specify the region (using the FIPS code for counties), and the quantiles from 1% ("q0.01") to 99% ("q0.99").</p> <p><em>This data is provided for non-commercial research and educational purposes.</em></p>
A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy
<p>The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF product is more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05.</p>
Pair Production PET Imaging to Detect Particle Distribution in Patients Undergoing Yttrium-90 Radioembolization
ClinicalTrials.gov study NCT02848638. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Incubation time as an important influence on egg production and distribution into clutches for sauropod dinosaurs
Open the record for dataset details and reuse information.
Data from: Regional productivity mediates the effects of grazing disturbance on plant cover and patch-size distribution in arid and semi-arid communities
Open the record for dataset details and reuse information.
Distributed F-IDF products
<p>This repository holds a distributed F-IDF product produced by CREST hydrologic model and Machine Learning method.</p>
Musculoskeletal Disorders of Workers in a Production and Distribution Company in Argentina
ClinicalTrials.gov study NCT04612153. IPD Sharing: NO. Countries: 1. Publications: 0.
Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)
Title: The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) - NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.
Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network
Inner product computation is an important primitive used in many techniques for feature dependency detection, distance computation, clustering and correlation computation among others. Recently, peer-to-peer networks are getting increasing attention in various applications involving distributed file sharing, sensor networks, and mobile ad hoc networks. Efficient identification of top few inner product entries from the entire inner product matrix of features in a distributed peer-to-peer network is a challenging problem since centralizing the data from all the nodes in a synchronous, communication efficient manner may not be an option. This paper deals with the problem of identifying significant inner products among features in a peer-to-peer environment where different nodes observe a different set of data. It uses an ordinal framework to develop probabilistic algorithms to find top-LaTeX elements in the inner product matrix. These l inner product entries are important in making crucial decisions about dependency or relatedness between feature pairs, important for a number of data mining applications. In this paper we present experimental results demonstrating accurate and scalable performance of this algorithm for large peer-to-peer networks and also describe a real-world application for this algorithm.
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.