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6,467 results for “process”
Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak
<p>Dataset of model simulations discussed in paper "Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak", <a href="https://doi.org/10.1016/j.atmosres.2022.106302">https://doi.org/10.1016/j.atmosres.2022.106302</a></p>
The dataset of Hong Kong Housing transaction records (1997-2018, after an incoming data quality processing)
<p>These detailed housing transaction records of over 2 million property rights entries in Hong Kong's property markets over the past 23 years (1997 - 2018).</p>
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Greece
<p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>contains the Greek questionnaire data set for Wave 1 of Work Pachage 7 of Catch-EyoU project (Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions; funded by the European Commission under the Horizon 2020 Programme; GA number: 649538; 2015-2018; http://www.catcheyou.eu/). This dataset contains the data underlying the publications presented in the References section (see below). </p> <p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>corresponds to the responses of participants to the questionnaire study for the purposes of Catch-EyoU project, WP7, Wave 1, collected in December 2016. These were entered and coded as either numeric or alphanumeric variables. Detailed information on variable names and labels is included in the portable file itself and it is also provided in the accompanying README file.</p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Ion Implantation Sensor and Process Target Data for Predicting Ion Beam Tuning in Semiconductor Manufacturing
<h2><strong>Dataset Description:</strong></h2> <p>This dataset is designed to predict ion beam tuning setup processes in semiconductor manufacturing, in terms of tuning success or failure, and tuning duration. It is split into <strong><code>X</code></strong> and <code><strong>y</strong></code> to allow for supervised learning approaches.</p> <ul> <li><code><strong>X</strong></code> represents the current equipment condition and the process targets of the currently processed and the upcoming lot, as defined within recipes.</li> <li><code><strong>y</strong></code> represents the ion beam tuning setup report, which informs about the tuning success ratio and tuning duration. These setups are necessary, when switching between recipes to prepare the equipment for processing the next lot. <strong><code>y</code></strong> contains three labels, enabling classification of (1) tuning success or fail, and (2) prolonged tuning, as well as (3) estimation of tuning duration as a regression task.</li> </ul> <p>About <strong><code>X</code></strong>:</p> <p>Each lot is processed with a specific recipe to achieve the process target. The tuning takes place before the first wafer of the to-be-tuned recipe is processed. Each row in <strong><code>X</code></strong> includes logistical information such as the equipment used for processing and parsed recipe / process target information for the current and upcoming lot. The majority of data consists out of aggregated metrics of equipment-internally tracked sensor traces, recording physical parameters such as gas flows, temperatures, voltages and currents. When analyzed in conjunction with the processed recipe, these sensors provide insights into the current equipment condition. </p> <p>About <code><strong>y</strong></code>:</p> <p>The <code>setup_result</code> column indicates the success or failure of tuning - with <code>setup_result=0</code> indicating tuning success, while <code>setup_result=1</code> signals tuning failure. If the first tuning attempt fails, there may be follow-up attempts, but these are not included in this dataset. The <code>duration</code> column represents the tuning duration in seconds, as used for regression analysis. The <code>duration_interval</code> column is a binary label for prolonged tunings, i.e. <code>duration_interval=1</code> for instances, which take more than 6 minutes to tune.</p> <p>For reproducibility of the corresponding paper's results:</p> <ol> <li>The dataset contains the same carefully curated subset of features.</li> <li>The train_test_split() has already been performed, thus we provide <code>x_train</code> and <code>x_valid</code> separately.</li> <li>To reduce the effect of outliers in the data, the sensor data has already been scaled, as derived from <code>x_train</code>.</li> </ol> <p>In summary, these datasets (<code><strong>X</strong></code>, <code><strong>y</strong></code>) provide comprehensive information for predicting ion beam tuning in semiconductor manufacturing, making it a valuable resource for researchers and practitioners in the field.</p> <h2><strong>Python Code for Reproducibility:</strong></h2> <p>Furthermore, we share a jupyter notebook <code>ionbeamtuning.ipynb</code> with Python code to train the best performing model on the provided data, as described in the paper. To execute the code, you may need to install any missing packages specified in the <code>requirements.txt</code>, as indicated within the notebook.</p>
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
<p>This upload contains the HZV029 Plasma and HZV029 Two-Phase dataset for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p> <p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p> <p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript re-submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p> <p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p> <p>https://github.com/shuzhao-li-lab/metDataModel</p> <p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p> <p><strong>Some information that is needed to rerun this analysis:</strong></p> <p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under sequence_files.zip. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p> <p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p> <p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline. MS2 annotation requires the files in the AcquireX directory which is MS2 acquisitions on pooled HZV029 plasma samples.</p> <p>For the comparison between MetaboAnalystR and the pcpfm, subsets of the datasets were used. These subsets and the sequence files are in Subsets_for_performance_testing.zip. The sequences are also in the sequence_files directory as well</p> <p>The notebooks reference data in the analysis folders. Copies of these files are located with the notebooks to ease reproduction of the exact results in the paper; however, to do so, you will need to change paths to this data in the notebook. This lets the notebooks be ran during a rerun without copying intermediates back and forth and it keeps the github repo clean.</p> <p><strong>Version History:</strong></p> <p>This version is after reviewer comments and is for resubmission.</p> <p> </p> <p><strong>Contributions:</strong></p> <p>Joshua M Mitchell implemented the pipeline and was first author on the manuscript. Shuzhao Li is the corresponding author on the manuscript. </p> <p>Maheshwor Thapa performed the experiments to collect the HZV029 data. Yuanye Chi helped with testing and documenting the pipeline. </p> <p>Jiangou (Jeff) Xia and Zhiqiang Pang provided the R portion of the analysis. </p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Raw and processed autofluorescence neuroimaging data
<p>Raw and processed <em>in vivo</em> mouse whole-cortex autofluorescence imaging data.</p> <p>This release accompanies the pub <a href="https://doi.org/10.57844/arcadia-b963-15ac">"Label-free neuroimaging in mice captures sensory activity in response to tactile stimuli and acute pain</a>.</p> <p>The raw data is split into five zipfiles because it is quite large (70GB total). Each of these five zipfiles corresponds to one of the five subdirectories in the single zipfile of processed data.</p>
Synthetic Dataset of charging processes by electric vehicles at workplace in Germany
<p>The dataset shows eight cluster groups that depict the mobility behavior of electric vehicle users in the employee context. For this purpose, 23.9 million data entries were analyzed, corresponding to 37,238 charging sessions. These data were collected over the year 2023. The 220 charging points were exclusively accessible to employees (private use case). From the data, cluster groups were derived using the Gaussian Mixture Model, and a synthetic dataset was generated through Monte Carlo sampling.</p> <p><span>The dataset consists of 8000 synthetic profiles, offering a robust scientific basis. By retaining the same statistical attributes as the empirical data, the synthetic profiles represent eight different mobility clusters, each containing 1000 entries, including full-time and part-time employees, shift workers, pool vehicle users, and opportunists.</span> Each cluster is represented by the mean parking start hours (arrival time - in decimal hours), mean parking duration (in decimal hours), the average energy recharged, and the average charging duration, each including the cluster-specific standard deviation and median.</p> <p>Further information can be obtained from the upcoming publication: "Synthetic Dataset of charging processes by electric vehicles at workplace in Germany."</p>
Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm
<p>Used data and obtained results for the paper Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm.</p> <p>The reader can find the following files :</p> <ul> <li>configuration_types.csv contains the cloud resource types (the name is the EC2 instance for database, and for the BPM engine separated by an underscore), their price and their capacity</li> <li>tenants_uni.csv contains the customers and their minimum and maximum BPM task throughput</li> <li>results_[number of tenants]_seg.csv files contain the results for the previous heuristic (segmentation only)</li> <li>results_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the iterative heuristic tests</li> <li>solver_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the restricted model solved tests</li> </ul>
Vagrant Lives: 14,789 Vagrants Processed by Middlesex County, 1777-1786 (version 1.2)
<p><em>This dataset should be used instead of the earlier version (https://zenodo.org/record/13103).</em></p> <p>This updated dataset makes accessible the uniquely comprehensive records of vagrant removal from, through, and back to Middlesex, encompassing the details of some 14,789 men and women removed (either forcibly or voluntarily) as undesirables between 1777 and 1786. In includes people ejected from London as vagrants, and those sent back to London from counties beyond. Significant background material is available on the 'London Lives' website, which provides additional context for these records. The authors also recommend the following article:</p> <p> Tim Hitchcock, Adam Crymble, and Louise Falcini, ‘Loose, Idle and Disorderly: Vagrant Removal in Late Eighteenth-Century Middlesex’, _Social History_.</p> <p>Each record includes details on the name of the vagrant, his or her parish of legal settlement, where they were picked up by the vagrant contractor, where they were dropped off, as well as the name of the magistrate who had proclaimed them a vagrant. Each entry is georeferenced, to make it possible to follow the journeys of thousands of failed migrants and temporary Londoners back to their place of origin in the late eighteenth century.</p> <p>Each entry has 31 columns of data, all of which are described in the READ ME file.</p> <p>The original records were created by Henry Adams, the vagrant contractor of Middlesex who had - as had his father before him - conveyed vagrants from Middlesex gaols to the edge of the county where they would be sent onwards towards their parish of legal settlement. His role also involved picking up vagrants on their way back to Middlesex, expelled from elsewhere, as well as those being shepherded through to counties beyond, as part of the national network of removal. Eight times per year at each session of the Middlesex Bench, Adams submitted lists of vagrants conveyed as proof of his having transported these individuals, after which he would be paid for his services. The dataset contains all 42 surviving lists out of a possible 65.The gaps in the records are unfortunately not evenly spaced throughout the year. We know more, for example, about removal in October than in May.</p> <p>Spellings have been interpreted and standardized when possible. Georeferences have been added when they could be identified. This dataset was created for 21st century historians, and should not be construed as a true transcription of the original sources. Instead the goal was to use a limited vocabulary and to interpret the entries rather than recreate them verbatim. While this is undesirable for anyone interested in spelling variations of names and place names in the eighteenth century, it is the authors' hope that these interpretations will make it easier to conduct quantitative analysis and studies in historical geography.</p> <p>This dataset has been published with additional contextual information in the <em>Journal of Open Humanities Data</em>. It can be found at the following location:</p> <p>Crymble, A, Falcini, L and Hitchcock, T 2015 Vagrant Lives: 14,789 Vagrants Processed by the County of Middlesex, 1777–1786. <em>Journal of Open Humanities Data</em> 1: e1, DOI: http://dx.doi.org/10.5334/johd.1</p>
Improving the drug discovery process by using multiple classifier systems
<p>High-quality dataset gathered from ChEMBL version 22 based on UniProt accession P34972. Regarding to activity data potential, duplicates were ignored, no activity or data validity comments were allowed, only data from binding assays and with a pCheMBL value were kept. This led to a dataset composed of 3925 chemical compounds (instances) represented using 2132 features. The first 2048 features epitomize different chemical structures fingerprints (represented using FCFP_6 notation), while the remaining 84 are associated with several physicochemical descriptors (such as Fractional Polar Surface Area, Rotatable Bonds or Molecular Weight). Finally, the set was transformed into a binary classification set where the activity cut-off was defined at a pChEMBL value > 7 and written to a tab-delimited text file. The final set contained 1977 active compounds and 1948 inactive compounds. Table 3 shows the codification of each feature grouped by type.</p>
Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process: dataset
<p>This is the dataset related to the article published in ACS Sustainable Chemistry & Engineering “Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process” DOI: 10.1021/acssuschemeng.8b01407</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Longitudinal Survey Data: Wave 1 & Wave 2: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ longitudinal survey that was carried out in Estonia from October 2016 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by Univversity of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Survey Data: Estonia: Wave 2
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from November 2017 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by University of Taartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Pilot Questionnaires: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from May to June 2016. It merges results of two polls (16-18 and 20-26 year olds). Survey was conducted by University of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</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.