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.
15,733
datasets available to search
ShareScore release 0.9.0
Dataset results
15,733 results for “article”
Bruker NMR data set for journal article: 3.4. Understanding the Microstructure Connectivity in Photopolymerizable Aluminum-Phosphate-Silicate Sol−Gel Hybrid Materials for Additive Manufacturing
<p>Solid state fast MAS 1H data for hybrid polymerizable compounds. </p>
Detailed list of all published articles related to Gaza in Fusha from October 7, 2023, to April 24, 2024
<p><span>A detailed list of all published articles related to Gaza in Fusha from October 7, 2023, to April 24, 2024, icluding the title of the articl, the date of publication, the writers name (with a hyperlink to the article), and the writers nationality or residency.</span></p>
Article: AI-MAPE Versus SA-SGM - Complete Data Bank
<p>Mechanistic models provide an in-depth understanding of important biophysical systems. In fields such as developmental biology, these models are inherently complex, as they require genuinely spatial-stochastic descriptions of the underlying systems. This complexity poses significant challenges for inferring model parameters. Recently, modern deep-learning techniques have been integrated with simulation-based inference, creating an exciting new approach for estimating parameters of such models. Their overall goal is to computationally replicate target empirical observations and to develop powerful prediction tools for uncovering hidden system dynamics. However, these modern approaches remain broadly general and are often difficult to implement for specific spatial-stochastic problems, particularly within developmental biology. This difficulty raises the question of how much more valuable these modern approaches are compared to classical techniques. In this study, we compare one modern approach, <em>AI-MAPE</em>, inspired by the sequential neural posterior estimation (SNPE) algorithm, against one classical approach, <em>SA-SGM</em>, inspired by the simulated annealing (SA) algorithm. Our findings show that, while the inferred parameter sets generally agree between the two approaches, the AI-powered method, at comparable computational effort, provides significantly richer and more regular inferred distributions. This results in more detailed information about parameter interactions and synergies than the SA-inspired method.</p>
Data related to article: Substantial contribution of transported emissions to organic aerosol in Beijing
<p>This dataset includes data related to the article "Substantial contribution of transported emissions to organic aerosol in Beijing" (https://doi.org/10.1038/s41561-024-01493-3). </p>
Dataset for the article "Benchmarks and Workflow for Harmonic IR and Raman Spectra"
<p>This dataset contains the data and scripts used in the article "Benchmarks and Workflow for Harmonic IR and Raman Spectra"</p>
Source data supporting the Article " Malaria transmission risk is projected to increase in the highlands of Western and Northern Rwanda"
<p>Average T<sub>min</sub>, T<sub>max</sub>, rainfall, malaria incedence in 30 districts were retrieved from the Rwanda Meteorological Agency and Rwanda’s Health Management Information System (HMIS) during 2010–2015. We applied an ensemble learning method, namely, Random Forest Model (RFM), to comprehensively estimate the effect of a changing climate on malaria incidence according to historical observations in Rwanda. Based on this forecasting model, we predict future malaria risk and its spatiotemporal changes under two distinct Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). </p>
Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em> and accompanies the main research data set (<a href="https://doi.org/10.5281/zenodo.10878070" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10878070</a>).</p> <p>In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.</p> <p>The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)</p> <p>The different data layers are:</p> <p><strong>replicate.US.R</strong> </p> <p> is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".</p> <p><strong>pointclouds_original.zip</strong></p> <p> is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).</p> <p><strong>processed_foranalysis.zip</strong></p> <p> is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".</p> <p><strong>summaries.zip</strong></p> <p><strong> </strong>is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".</p> <p><strong>figures.zip</strong></p> <p> is a set of figures displayed in the Supplementary Material of the paper <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em>.</p>
Data article: Distributed PV power data for three cities in Australia.
<p>This dataset is as presented in the paper titled "Data article: Distributed PV power data for three cities in Australia." in the Journal of Renewable and Sustainable Energy, volume 11 by Jamie M, Bright, Sven Killinger and Nicholas A. Engerer.<br> </p> <p><strong>Abstract:</strong><br> We present a publicly available dataset containing photovoltaic (PV) system power measurements and metadata from 1,287 residential installations across three states/territories in Australia--- though mainly for the cities of Canberra, Perth and Adelaide. <br> The data is recorded between September 2016 and March 2017 at 10-min temporal resolution and consists of real inverter reported power measurements from PV systems that are well distributed throughout each city. The dataset represents a considerably valuable resource as public access to spatio-temporal PV power data is almost non-existent; this dataset has been used in numerous articles already by the authors. The PV power data is free to download and is available in its raw, quality controlled (QC) and `tuned' formats. Each PV system is accompanied by individual metadata including geolocation, user reported metadata and simulated parameterisation. Data provenance,download, usage rights and example usage are detailed within. <br> Researchers are encouraged to leverage this rich spatio-temporal dataset of distributed PV power data in their research.</p> <p>Further information is available at <a href="https://dx.doi.org/10.25911/5ca6a0640869a">ANU Data Commons</a> and <a href="https://solcast.com.au/rooftop-solar/publication-of-a-research-grade-solar-pv-power-dataset/">Solcast</a>. <br> This dataset has an embargo period for 3 years after the ARENA funded ANU project closure, though data is always available through <a href="https://solcast.com.au/rooftop-solar/publication-of-a-research-grade-solar-pv-power-dataset/">Solcast</a>.</p> <p><br> <strong>Usage rights:</strong></p> <p>There is a non-standard data usage rights agreement for this data. In the uploads is a 'license and metadata.txt' file that details the usage rights and metadata of the data. The exact agreement is reproduced here:<br> <br> <em>The data is released with bespoke terms. We state the crucial elements of these terms here. The dataset is freely provided to researchers as is with no guarantee of support. The dataset is not for commercial usage, but for research only. You are empowered to use this dataset however you wish in your research, through direct usage, adaptation, or improvements to the data itself. The data must not be redistributed, the access point for the data is exclusively through the website as described in Sec.III of the manuscript. Should you make significant changes to the data and wish to redistribute the new data, explicit permission must be obtained from the authors. Finally, appropriate accreditation to the creators must be made in all publications and outputs that arise from using this dataset in any way. To appropriately accredit the creators, we require that this exact data article (Bright et al., 2019) is referenced alongside its DOI: https://dx.doi.org/10.25911/5ca6a0640869a. Additionally, if using the QC version of the data, we also require a citation for the original papers detailing QCPV (Killinger et al., 2016a, 2016a). Furthermore, if using the tuned PV version of this data, we also require a citation for both the QCPV papers above and the PV tuning papers (Killinger et al., 2016b, 2017b) for full visibility of the data provenance. Lastly, the original hosts of this data PVoutput.org should be recognised for their efforts.</em></p> <p><em>References:</em></p> <p><em> Bright, Jamie M.; Killinger, Sven; and Engerer, Nicholas A. 2019. Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy. Vol 11. See online for full details.<br> <br> Killinger, Sven; Braam, Felix; Muller, Bjorn; Wille-Haussmann, Bernhard and McKenna, Russell, 2016a. Projection of power generation between differently-oriented PV systems. Solar Energy. 136, 153-165.</em></p> <p><em> Killinger, Sven; Muller, Bjorn; Saint-Drenan, Yves Marie and McKenna, Russell. 2016b. Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior. Conference Record of the IEEE Photovoltaic specialists Conference, pages 980-985.10.1109/PVSC.2016.7749757</em></p> <p><em> Killinger, Sven; Engerer, Nicholas and Müller, Björn. 2017a. QCPV: A quality control algorithm for distributed photovoltaic array power output. Solar Energy. 143, 120-131.</em></p> <p><em> Killinger, Sven; Bright, Jamie M.; Lingfors, David and Engerer, Nicholas A. 2017b. A tuning routine to correct systematic influences in reference PV systems’ power outputs. Solar Energy. 157, 6.</em></p> <p> </p>
SCB model data for article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'
<p>The SCB model simulation data for the article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'.</p> <p>The 'Rxx_A xx_Sxx_Lxxparameters' files are the data files, where Rxx is the beta_0 value, Axx is the A_e value, Sxx is the sigma_0 value and Lxx is the L_0 value (see the article for the definition of the values)</p> <p>The 'SCB_variables.xlsx' is a table to describe the variables in the data file.</p> <p>The 'read_scb.pro' is an IDL program to read the data files.</p>
Manual cross-validation data for the article: "Comparison of bibliographic data sources: Implications for the robustness of university rankings"
<p>These are sets of data collected from the manual cross-validation of DOIs (and related research outputs) that are sampled from Web of Science (WoS), Scopus and Microsoft Academic (MSA). For each of the 15 universities, we initially collect all DOIs indexed by each of the three bibliographic sources. Subsequently, we randomly sample 40, 30 and 30 DOIs from sets of DOIs that are exclusively indexed by WoS, Scopus and MSA, respectively, for each university. A manual cross-validation process is then followed to validate certain characteristics across the data sources. This cross-validation process was carried out by a data wrangler, on a part-time basis over a few months, for which online data was accessed from 18 December 2018 to 20 May 2019.</p>
Lume's journal articles relationships
<p>Citation relationships from Lume's journal article community</p> <p><a href="https://lume.ufrgs.br/handle/10183/19209">https://lume.ufrgs.br/handle/10183/19209</a></p>
Dataset related to article "Apparent Diffusion Coefficient standardization: multi-center and multi-vendor validation study across 1.5T and 3T scanners in a diffusion MRI phantom"
<p>The dataset includes ADC measurements collected for MRI harmonisation across centers and vendors, performed as part of the multicenter RESPECT project, using the QIBA-NIST diffusion MRI phantom.</p> <p>The dataset includes the following sheets:</p> <p>"ADC" sheet: ADC measurements (averaged over 4 repeated measures) for each scanner, comprising both first and second acquisition and 3 imaging planes. </p> <p>"temperature" sheet: temperature measurement of the phantom for each acquisition session.</p> <p>"repeatability" sheet: ADC measurements of the 4 repeated measures for the first acquisition. Average values and CVs are also reported.</p>
Dataset for the article "Electron-Transfer-Mediated by Detonation Nanodiamonds for Enhanced Cortisol Immunoassay"
<p>Dataset for the article "Electron-Transfer-Mediated by Detonation Nanodiamonds for Enhanced Cortisol Immunoassay".</p> <p>Chakavak Esmaeili1*, Jaroslav Kuliček1, Markéta Šlapal Bařinková1, Bohuslav Rezek1*</p> <p>1 Faculty of Electrical Engineering, CTU in Prague, Technicka 2, 16627 Prague, Czech Republic</p> <p>Figure 1. The preparation and immobilization step of the cortisol fabrication based on electrochemical immunosensor <br>Figure 2. 3D AFM images of (1) carbon bare electrode, (2) carbon electrode /DND, (3) carbon electrode/DND/EDC NHS (4) carbon electrode/ DND /EDC NHS/Ab, (5) carbon electrode/ DND /EDC NHS/Ab /BSA, (6) carbon electrode/ DND /EDC NHS/Ab/BSA/Ag<br>Figure 3. Raman spectra of immunosensor composition<br>Figure 4. FTIR spectra of DND (gray line), DND/ EDC NHS (blue line), DND/EDC NHS/Ab (green line). All spectra were recorded in the region between 4000–800 cm−1 with a resolution of 4 cm− 1 and 128 scans<br>Figure 5. Functionalization step. (1) carbon bare electrode, (2) carbon electrode /DND, (3) carbon electrode /DND/EDC NHS, (4) carbon electrode/DND/ EDC NHS/Ab and sensing step (5) carbon electrode/DND/ EDC NHS/Ab/BSA and (6) carbon electrode/DND/ EDC NHS/Ab/BSA/Ag in the presence of 100 nM cortisol in pH 7.0, containing 250 mM KCl and 5 mM K3[Fe(CN)6] and 5 mM K4[Fe(CN)6], Scan rate 0.1Vs−1<br>Figure 6. (a) Calibration curve of DPV with varying Ag concentrations. (b) Sensitivity comparison between configurations without Ab receptor (carbon electrode/DND/EDC NHS/Ag) and with Ab receptor (carbon electrode/DND/EDC NHS/Ab/BSA/Ag) in PBS solution, pH 7.0, containing 250 mM KCl and 5 mM K3[Fe (CN)6] and 5 mM K4[Fe (CN)6]<br>Figure 7. Interference study involving progesterone (2430 pg/ml), β-oestradiol (32 pg/ml), cortisone (28.6 nM), and corticosterone (3.94 nM) with respect to cortisol (100nM)<br>Figure 8. Stability of the immunosensor with repeated measurements of cortisol</p> <p> </p> <p>Figure S1. Optical images obtained for (1) bare carbon electrode, (2) DND, (3) DND/EDC NHS, (4) DND/EDC NHS/Ab, (5) DND/EDC NHS/Ab/BSA, (6) DND/EDC NHS/Ab/BSA/Ag.<br>Figure S2. SEM images of (1) bare carbon SPE, (2) carbon electrode/ DND, (3) carbon electrode/ DND /EDC NHS, (4) carbon electrode/ DND /EDC NHS/Ab, (5) carbon electrode/DND/EDC NHS/Ab/BSA, (6) carbon electrode/ DND /EDC NHS/Ab/BSA/Ag<br>Figure S3. (a) CV and (b) DPV of carbon bare electrode, carbon electrode /EDC NHS, carbon electrode/EDC NHS/Ab, carbon electrode/EDC NHS/Ab/BSA, carbon electrode/EDC NHS/Ab/BSA/Ag, in the presence of 100 nM cortisol, pH 7.0, containing 250 mM KCl and 5 mM K3[Fe(CN)6] and 5 mM K4[Fe(CN)6], Scan rate 0.1Vs−1.</p> <p><br>Table 1. Determination of cortisol in artificial saliva sample (n = 3)<br>Table S1. Surface roughness of immunosensors</p>
Human-labeled corpus of news articles, containing issue and game frames
<p>A human-labeled corpus of online news articles from New York Times and Los Angeles Times, containing issue and game frames, annotated with a great range of linguistic features at the following levels: syntactic (both form and function levels), semantic, semantic-syntactic, and pragmatic. Our specialized corpus has been created carefully to represent the written online media language regarding political news on the following topics: USA elections 2020, Donald Trump's impeachment, the Armenian genocide, Greta Thunberg, and Taiwan's elections. In total, 100 news articles were extracted, including image descriptions and titles that seemed relevant for our analysis. </p>
PS-BBICS: Pulse Stretching Bulk Built-in Current Sensor for On-chip Measurement of Single Event Transients (raw data from journal article)
<p>This upload contains raw data from the manuscript "PS-BBICS: Pulse Stretching Bulk Built-in Current Sensor for On-chip Measurement of Single Event Transients". The manuscript was published in Microelectronics Reliability, vol. 138, 2022.; DOI: https://doi.org/10.1016/j.microrel.2022.114726</p> <p>The upload consists of .pdf file of the manuscript and .opj files with raw data related to the figures in the manuscript. </p> <p>This work was supported in part by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558).</p>
A Design Concept for Radiation Hardened RADFET Readout System for Space Applications (raw data from journal article)
<p>This upload contains raw data from the manuscript "A Design Concept for Radiation Hardened RADFET Readout System for Space Applications". The manuscript was published in Microprocessors and Microsystems, vol. 90, 2022.; DOI: 10.1016/j.micpro.2022.104486</p> <p>The upload consists of .pdf file of the manuscript and .opj files with raw data related to the figures in the manuscript. </p> <p>This work was supported in part by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558).</p>
Supplementary Materials for article entitles 'Nominal and verbal syntax in translation and interpreting. Evidence from English speeches made in the European Parliament and their German translations and interpretations', submitted to Languages
<p>The Supplementary Materials contain the transcriptions (raw and tagged, 'sample_df.tsv'), the data frames with POS-frequencies, with ('pos_freqs_PART_split.tsv') and without ('pos_freqs.tsv') the PART-split in the German data, the data frames for the identification of interpreters ('voice_embeddings.csv'), an R-script for the statistical analysis and generation of plots ('stats.R') as well as the plots (folder 'plots').</p>
Dataset related to article: "Development of a nanocarrier-based approach for the blood-brain barrier passage in a murine model of amyotrophic lateral sclerosis"
<p>Raw data figures 3, 4, 5, 6</p>
the raw data of the simulations in the article.
<p>the raw data of the simulations in the article.</p>
Fading of pMOS dosimeters over a long period of time (raw data from journal article)
<p>This upload contains raw data from the manuscript "Fading of pMOS dosimeters over a long period of time". The manuscript was published in the Micro & Nano Letters, 2022; DOI: https://doi.org/10.1049/mna2.12119</p> <p>The upload consists of a .pdf file of the manuscript and .opj files with raw data related to the figures in the manuscript. </p> <p>This work was partly supported by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011).</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.