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.
3,443
datasets available to search
ShareScore release 0.7.1
Dataset results
3,443 results for “technology”
INTEND D 2.1 Transport projects & future technologies synopses database
<p>This excel file provides the database that contains all the project synopses that were carried out in the INTEND D 2.1 Transport projects & future technologies handbook deliverable. The reviews are divided into transport modes and contain the technology themes that were identified and brief summaries of what each project that was reviewed had researched. The database contains a total of 354 transport projects that have carried out hard technology research, predominantly funded under FP7 (2010-2014), all H2020 projects that have been funded as well as other international projects.</p>
Data of publication Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies
<p>Data corresponding to the figures of the publication " Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies" by S. Liu et al. (https://pubs.rsc.org/en/content/articlelanding/2018/ra/c8ra07246a#!divAbstract). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
Prosumer technology database
<p>Set of three databases of technologies compatible with the Strategic Energy Technologies Information System (SETIS) and its various tools. It includes energy production, energy demand, demand side management and energy storage both in terms of electricity and heat as well as current and future technologies.</p>
Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment, Supporting Information
<p>This repository contains the dataset and analysis scripts associated with the upcoming publication titled <em>Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment</em>. The repository includes a comprehensive collection of data and scripts related to optical tweezer experiments, growth assays, qPCR data, and supplementary information. It is organized into several sections, each detailing different aspects of the study:</p> <ul> <li><strong>Growth Assays:</strong> Includes raw and processed data on parasitemia levels, invasion rates, and growth rate assays, along with corresponding Jupyter notebooks and Python scripts for data visualization (e.g., <code>GrowthAssayPlotlib.py</code>, <code>Plot GA1.ipynb</code>, and <code>GA2_df_melted.json</code>).</li> <li><strong>qPCR Data:</strong> Contains results from multiple qPCR runs, including quantification data for various samples, as well as analysis scripts and plotted results (<code>qpcr_plotbench.ipynb</code>, <code>qPCR_plotting.py</code>, etc.). Data files such as <code>.xlsx</code> and <code>.json</code> contain gene expression data and fold changes to NF54.</li> <li><strong>Optical Tweezer Experiments:</strong> Includes detailed results and plots from optical tweezer measurements of attachment forces, time dependence, and multiple merozoite attachments. Notebooks (<code>tweezer_plots.ipynb</code>, <code>Antibody_binding_assay_plots.ipynb</code>) and data files support these analyses.</li> <li><strong>Optical Tweezer Images</strong>: Includes images that were used to measure RBC diameters for deformation and force measurements in <code>.tiff</code> format.</li> <li><strong>Supplementary Information (SI):</strong> Provides additional data and visualizations, such as scatter plots of two stretched RBCs, time post-egress vs. detachment force, and antibody GIA flow data. The accompanying figures (e.g., <code>SupFig1d_egress time vs force_3D7.svg</code>, <code>SupFig5a_GIA.svg</code>) are provided as <code>.svg</code> files.</li> </ul> <p>This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights.</p>
Data of the publication Rare Earth‐Diamond Hybrid Structures for Optical Quantum Technologies
<p>Data of the publication published under the reference: I.G. Balașa et al., Advanced Optical Materials, 2401487 (2024).</p>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase
<p>We provide here the underlying data of the publication "Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase". Please find the abstract below.</p> <p>Advanced biotransformation processes typically involve the upstream processing part performed continuously and interlinked tightly with the product isolation. Key in their development is a catalyst that is highly active, operationally robust, conveniently produced and recyclable. A promising strategy to obtain such catalyst is to encapsulate enzymes as permeabilized whole cells in porous polymer materials. Here, we show immobilization of the sucrose phosphorylase from Bifidobacterium adolescentis (P134Q-variant) by encapsulating the corresponding E. coli cells into polyacrylamide. Applying the solid catalyst, we demonstrate continuous production of the commercial extremolyte 2-α-D-glucosyl-glycerol (2-GG) from sucrose and glycerol. The solid catalyst exhibited similar activity (≥70%) as the cell free extract (~800 U g-1 cell wet weight) and showed excellent in-operando stability (40 °C) over 6 weeks in a packed-bed reactor. Systematic study of immobilization parameters related to catalyst activity led to the identification of cell loading and catalyst particle size as important factors of process optimization. Using glycerol in excess (1.8 M), we analyzed sucrose conversion dependent on space velocity (0.075 – 0.750 h-1) and revealed conditions for full conversion of up to 900 mM sucrose. The maximum 2-GG space-time yield reached was 45 g L-1 h-1 for a product concentration of 120 g L-1. Collectively, our study establishes a step-economic route towards a practical whole cell-derived solid catalyst of sucrose phosphorylase, enabling continuous production of glucosides from sucrose. This strengthens the current biomanufacturing of 2-GG, but also has significant replication potential for other sucrose-derived glucosides, promoting their industrial scale production using sucrose phosphorylase. </p>
GENeSYS-MOD Germany: Technology, demand, and renewable data
<p>This dataset contains renewable potentials, timeseries, technology data, and additional data tables for the current implementation of GENeSYS-MOD Germany.</p>
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment
<p>This dataset comprises the sequence of <strong>44 278 RNA oligonucleotide "baits" (120 bp each) </strong>designed to perform <strong>whole-genome capture and sequencing of <em>Mycobacterium tuberculosis</em> directly from clinical samples</strong> (DNA) using Agilent Technologies’ SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol. </p> <p>RNA oligonucleotide “baits” were designed to span the ∼4.5 Mb of the <em>M. tuberculosis</em> genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was <em>in silico</em> fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture.</p> <p>More details can be found in the following publication:</p> <p>- Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., & Gomes, J. P. (2023). Molecular Capture of <em>Mycobacterium tuberculosis</em> Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences. <em>International journal of molecular sciences</em>, <em>24</em>(3), 2912. https://doi.org/10.3390/ijms24032912</p>
Fine-mapped summary statistics for protein coding regions (i.e. cis regions) based on the Olink Explore 1536 and Explore Expansion technologies
<p>This data set contains fine-mapping results performed by SuSie for cis regions ±500kb around the protein coding gene) for protein targets as measured by the Olink Explore 1536 and Explore Expansion technologies in 1,180 individuals from EPIC Norfolk study (https://www.epic-norfolk.org.uk/). Only protein targets where fine-mapping predicted at least one credible set were included in the results. </p>
RDF dataset produced in the work "Exploring Adverse Outcome Pathways for Nanomaterials with semantic web technologies"
<p>Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge.</p>
Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies
<p><strong><em>Supplementary Material - Filled LCI data collection schemes </em></strong>from the publication <em><strong>"Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies"</strong></em>. This repository includes LCI data for three time horizons; short-term, medium-term, and long-term.</p> <p>In the <strong>short-term time horizon</strong>, LCI data for the following technologies distinguished according to two different configurations (conventional and GT-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_short-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_short-term_GT-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_short-term_v01.xlsx)</li> <li>Battery EOL (GENESIS_LCI_battery_EoL_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery Li-ion (GENESIS_LCI_battery_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_short-term_v01.xlsx)</li> <li>Power electronics and drives (GENESIS_LCI_power_elec_drives_short-term_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_short-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_short-term_GT-bat_v01.xlsx)</li> <li>SAF (GENESIS_LCI_SAF_short-term_v01.xlsx)</li> </ul> <p>In the <strong>medium-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, GT-bat, and PEMFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_medium-term_v01.xlsx)</li> <li>Battery EOL Li-S GT-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery EOL Li-S PEMFC-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-S GT-bat (GENESIS_LCI_battery_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery Li-S PEMFC-bat (GENESIS_LCI_battery_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_medium-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_medium-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage (GENESIS_LCI_H2_onboard_storage_medium-term_PEMFC_v01.xlsx)</li> <li>Power electronics and drives GT-bat (GENESIS_LCI_power_elec_drives_medium-term_GT-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_medium-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_medium-term_GT-bat_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_medium-term_PEMFC-bat_v01.xlsx)</li> </ul> <p>In the <strong>long-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, PEMFC-bat, and SOFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_long-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_long-term_PEMFC-bat_v01.xlsx)</li> <li>Airframe SOFC-bat (GENESIS_LCI_airframe_long-term_SOFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_long-term_v01.xlsx)</li> <li>Battery EOL Li-Air PEMFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery EOL Li-Air SOFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery Li-Air PEMFC-bat (GENESIS_LCI_battery_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-Air SOFC-bat (GENESIS_LCI_battery_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_long-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_long-term_PEMFC-bat_v01.xlsx)</li> <li>Fuel cell SO (GENESIS_LCI_fuel cell_SO_long-term_SOFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage PEMFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage SOFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_SOFC-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_long-term_PEMFC-bat_v01.xlsx)</li> <li>Power electronics and drives SOFC-bat (GENESIS_LCI_power_elec_drives_long-term_SOFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_long-term_conventional_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_long-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant SOFC-bat (GENESIS_LCI_powerplant_long-term_SOFC-bat_v01.xlsx)</li> </ul> <p>Additionally, the following file is used for <strong>all time horizons</strong>:</p> <ul> <li>H<sub>2</sub> production and supply (GENESIS_LCI_H2_production_&_supply_v01.xlsx)</li> </ul>
Dataset for publication "Acid leaching technology for post-consumer gypsum purification"
<p>This dataset contains underlying and extended data of publication entitled "Acid leaching technology for post-consumer gypsum purification". </p> <p>Data comprises: </p> <p>- Dataset description.txt (abbreviations and codes of samples presented in the publication)</p> <p>- Particle size analysis.zip (particle size distribution data files for the two acid leaching feedstocks in .txt format)</p> <p>- SEM.zip (scanning electron microscopy images of gypsum from refurbishment plasterboard waste before and after acid leaching in .jpg format)</p> <p>- TGA.zip (thermal gravimetric analysis raw data for gypsum from refurbishment and demolition plasterboard wastes before and after acid leaching in .xlsx format) </p> <p>- XRD.zip (X-ray diffraction data files for samples presented in the publication in .raw, .brml and .uxd formats)</p> <p>- XRF.zip (X-ray fluorescence data files for samples presented in the publication in .xlsx format)</p> <p>- Extended data.pdf (additional tables and figure supporting claims in the publication).</p>
Science, Technology & Society Eurobarometers 1993 - 2021: Trend Data Collection
<p>These files contain structured collections of Eurobarometer (EB) survey data from 1993 to 2021, focusing on European citizen’s views on science and technology (S&T). The primary aim of these data collections is to facilitate research on trends over time regarding people’s knowledge, perception and attitudes towards S&T. The European Union has collected extensive survey data from the general public over the past 50 years through its official polling instrument, the Eurobarometer. Its general goal is monitoring the state of public opinion on diverse subjects and issues throughout Europe, one of which is S&T. The data collection files, provided in the folder ‘EB_data_csv’, include data from seven different Eurobarometer surveys. These surveys were selected because their raw datasets were openly accessible through Open EU Datasets. To ensure sufficient data points for plotting specific trends over time, we included only survey questions that appeared in at least three different EB surveys. </p>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
Teaser SUNRISE releases its technological roadmap to a clean energy EU
<p>Promotional video roadmap: We are thrilled to present our freshly released technological roadmap! The SUNRISE technological roadmap is the result of the integrated knowledge of a broad group of scientists across Europe and a key step to engage the whole community towards building a climate neutral EU. This roadmapping process was launched in May 2019 by a dedicated working group within the SUNRISE consortium, collecting and analyzing broad input from over 180 stakeholders at the SUNRISE Stakeholder Workshop on 17-18 June, 2019, in Brussels.</p>
Closure of the 9th European Summer University in Digital Humanities "Culture & Technology"
<p>Closure of the 9th European Summer University in Digital Humanities "Culture & Technology"</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.