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Dataset results
174 results for “online data”
First results of online compression of HEP data using Baler
<p>Within the framework of the HiDA Trilateral Data Science Exchange Program, this internship project unveils preliminary findings on online compression using Baler. It involved the examination of various datasets of different sizes from the High Energy Physics (HEP) domain to evaluate compression performance. All datasets used are subsets of the jet data recorded by the CMS experiment at the LHC in 2012, released as open data under the Creative Commons CC0 waiver (see references). The data is modified (flattened, truncated, formatted, etc.) and packaged in a way that makes it easy for others to reproduce the results. The provided files in this page include comprehensive instructions for replicating the project's results, as well as datasets and outcomes. Below, you'll find a brief overview of the project's folder structure, categorized by the dataset utilized (small dataset/example CMS data/larger CMS data), the online/offline compression method, and resource utilization, particularly regarding GPU usage.</p> <p>Presentations summarizing this project's results can be found here: https://zenodo.org/record/8326707.</p> <p><strong>Project's folders:</strong></p> <ol> <li> <p><strong>Reproduction Instructions</strong>: This folder houses all files that offer detailed guidelines for replicating the project's presented results. These files serve as a reference for accessing relevant materials.</p> </li> <li> <p><strong>GPU with Example CMS Data.zip</strong>: This directory contains all files related to offline compression of the approximately 100MB example CMS dataset provided by Baler. GPU resources were employed in the model training process.</p> </li> <li> <p><strong>GPU with Larger CMS Data (1).zip</strong>: In this section, you'll find files associated with the compression of a larger CMS dataset, approximately 1.4GB in size. It includes results of offline compression and a split of the dataset into a 50/50 ratio for training and testing, with results provided for various epochs.</p> </li> <li> <p><strong>GPU with Larger CMS Data (2)</strong>: This folder holds the larger dataset, divided into two halves, with the first half's array values in one file and the second half's in another.</p> </li> <li> <p><strong>Offline/Online on Small Dataset</strong>: Here, you'll find files related to both offline and online compression of a small dataset, roughly 100KB in size, extracted from the example CMS dataset provided by Baler.</p> </li> <li> <p><strong>Modifications of Small Dataset</strong>: This section comprises variations of the small dataset, including both normalized and un-normalized datasets.</p> </li> <li> <p><strong>Materials</strong>: This folder includes fundamental papers and summaries to enhance your understanding of the project.</p> </li> <li> <p><strong>HiDA</strong>: Within this directory, you'll find a printed webpage from the HiDA program.</p> </li> </ol>
Data and Supplement from: Phylogenetic tree instability after taxon addition: Empirical frequency, predictability, and consequences for online inference
Open the record for dataset details and reuse information.
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
Open the record for dataset details and reuse information.
Linking Twitter & Survey Data [online workshop]
<p>Twitter has become an important data source for many social scientists. While using data from Twitter can help to avoid or alleviate some of the issues related to self-report data, such as social desirability or problems with recollection, Twitter data have their own sets of limitations, including the lack of information about individuals or missing outcome variables of interest. Linking data from surveys and Twitter is a way to combine the unique strengths of the two data types and overcome some of their respective limitations. The online workshop led by Luke Sloan (Cardiff University), Johannes Breuer (GESIS), and Libby Bishop (GESIS) covered the key steps in the research process of linking Twitter and survey data. Specifically, the workshop addressed the phases of study planning, (Twitter) data collection, data processing, and archiving and sharing. The workshop drew on recent experiences from different studies that the instructors were involved in and provided guidance on how to address the ethical and operational issues associated with linking Twitter and survey data in the different phases of the research process.</p> <p>The video is also available for viewing on <a href="https://youtu.be/IG1jJ3WP2h8">YouTube</a>.</p>
Data from: An R package and online resource for macroevolutionary studies using the ray-finned fish tree of life
1. Comprehensive, time-scaled phylogenies provide a critical resource for many questions in ecology, evolution, and biodiversity. Methodological advances have increased the breadth of taxonomic coverage in phylogenetic data; however, accessing and reusing these data remain challenging. 2. We introduce the Fish Tree of Life website and associated R package fishtree to provide convenient access to sequences, phylogenies, fossil calibrations, and diversification rate estimates for the most diverse group of vertebrate organisms, the ray-finned fishes. The Fish Tree of Life website presents subsets and visual summaries of phylogenetic and comparative data, and is complemented by the R package, which provides flexible programmatic access to the same underlying data source for advanced users wishing to extend or reanalyze the data. 3. We demonstrate functionality with an overview of the website, and show three examples of advanced usage through the R package. First, we test for the presence of long branch attraction artifacts across the fish tree of life. The second example examines the effects of habitat on diversification rate in the pufferfishes. The final example demonstrates how a community phylogenetic analysis could be conducted with the package. 4. This resource makes a large comparative vertebrate dataset easily accessible via the website, while the R package enables the rapid reuse and reproducibility of research results via its ability to easily integrate with other R packages and software for molecular biology and comparative methods.
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
Survey data from stakeholders on the supply side of Nigeria's online budget
<p>The survey data was collected during field work from key personnel on the supply side of the national budget preparation process in Nigeria. This was done as part of the stakeholder survey on the case study investigating the use of the online national budget of Nigeria which is part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
Social media data from online discourse on Nigeria's budget
<p>The social media data was obtained by extracting online discourse on Nigeria's 2013 budget. This was done as a component of the media analysis on the case study investigating the use of the online national budget of Nigeria which is part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
Survey data from stakeholders on the demand side of Nigeria's online budget
<p>The survey data was collected during field work from key personnel on the demand side of the national budget preparation process in Nigeria. This was done as part of the stakeholder survey on the case study investigating the use of the online national budget of Nigeria which is part of the 'Exploring the Emerging Impacts of Open Data in Developing Countries' (ODDC) research project.</p>
Lab-scale membrane bioreactor (MBR) high-frequency online data
<p>This dataset is composed of 4 periods of more or less continuous operation, but with varying length. The measured online data, with a frequency of per second, are stored chronologically in 4 compressed csv-files 'Data_MBR_Period_x'. The installation was running with some fixed and variable settings. The variable settings are stored in 'Settings_MBR'. They were only logged when changed, making this a small file. All additional information on the used sensors etc. is listed in the 'Metadata'-file, including the values of the fixed settings. Finally, 'Schematic.png' gives an overview of the MBR setup and the location of the sensors.</p>
Data from: Effectiveness of Online Off-the-Job Training in Attracting Participants and Video-On-Demand Streaming in Improving Work-Life Balance: A Study Focusing on Medical Technologists
<p>The Nara Association of Medical Technologists has introduced online Off-Job Training (Off-JT) starting from FY2020 in response to the COVID-19 pandemic. This study aims to evaluate the online Off-JT, which differs from the traditional face-to-face format. Firstly, we compared the online format's ability to attract participants with the face-to-face format based on the number of training sessions and attendees. Despite having fewer training sessions (40.8% less), the online format had an average attendance of 105.4% higher (39.7 vs. 19.3) than the face-to-face format. To enhance participant convenience, we offered a limited number of live and video-on-demand (VOD) sessions on YouTube, evaluating their usefulness through an online survey focusing on work-life balance (WLB). The survey results showed that 81.9% (458/559) of respondents reported an improvement in WLB. The effect on WLB improvement varied depending on the viewing method, with VOD sessions showing 84.1% (376/447) and live sessions showing 73.2% (82/112). We believe that the increased ability to attract participants in the online Off-JT is mainly due to the elimination of travel burdens through internet-connected devices. The combination of live and VOD sessions on YouTube allowed participants to adjust their viewing time, leading to better allocation of free time and improved WLB. The online Off-JT and VOD delivery have shown to enhance convenience for participants by removing geographical and time constraints, resulting in positive effects.</p>
Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"
<h1>Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"</h1> <p>DOI: https://doi.org/10.1038/s41467-024-54520-7</p> <h2>Authors</h2> <ul> <li>Franco De Palma</li> <li>Fabian Oppliger</li> <li>Wonjin Jang</li> <li>Stefano Bosco</li> <li>Marián Janík</li> <li>Stefano Calcaterra</li> <li>Georgios Katsaros</li> <li>Giovanni Isella</li> <li>Daniel Loss</li> <li>Pasquale Scarlino</li> </ul> <h2>Description</h2> <p>The data for all figures in the main text can be found in csv files in ASCII format in the corresponing folders. For Figures 4-6, the panels are numbered from top to bottom.</p>
Data archive for the peer-reviewed journal article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions"
<p>This data archive accompanies the article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions", which was accepted in November 2024 in the peer-reviewed journal Environmental Science and Technology Letters. The data archive contains the processed OP_DTT, PM loading, oxidant level, and elemental ratio measurements presented in this journal article. </p>
User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops
<p><strong>Introduction</strong></p> <p>This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article. </p> <p><strong>Summary of the user evaluation</strong></p> <p>The structrue of the user evalution is summarized in the table below:</p> <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table> <p> </p> <p><strong>The structure of the data is as follows:</strong></p> <p>1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey:</p> <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> <p>2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>): </p> <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> <p><em>Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details</em></p>
African wood density database with matches to the taxonomic backbone data sets of World Flora Online (version 2023.12) and the World Checklist of Vascular Plants (version 11)
<p>The <strong><span>African Wood Density Database </span></strong><span>provides air-dry wood density data for over 750 tree species grown in Africa.</span></p> <p>This archive provides taxonomic matches with recent versions of <strong>World Flora Online</strong> (WFO; <a href="../records/10425161">version 2023.12 downloaded from Zenodo</a>; Borch et al. <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP; <a href="https://sftp.kew.org/pub/data-repositories/WCVP/Archive/">version 11 downloaded from the Kew data depository</a>; Govaerts et al. <a href="https://doi.org/10.1038/s41597-021-00997-6">2021</a>). Matching was done via the <strong>WorldFlora</strong> package (<a href="https://cran.r-project.org/package=WorldFlora">version 1.14-3</a>; Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>), using similar scripts as documented in this Rpub: <a href="https://rpubs.com/Roeland-KINDT/1134151">https://rpubs.com/Roeland-KINDT/1134151</a>.</p> <p> </p> <ul> <li><span>Carsan, S. Orwa, C. Harwood, C. Kindt, R. Stroebel, A. Neufeldt, H. and Jamnadass, R. 2012. African Wood Density Database. World Agroforestry Centre, Nairobi. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">https://apps.worldagroforestry.org/treesnmarkets/wood/#</a> </span></li> <li><span>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></span></li> <li><span>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></span></li> <li><span>Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. <em>Applications in Plant Sciences</em> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></span></li> </ul> <p> </p> <p>Original funding for the database was provided <span>by the Carbon Benefits Project (CBP) supported by The Global Environment Facility (GEF). Development of the 2024 version </span>was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>. When using <strong>African Wood Density database</strong> in your work, cite the 2012 version (Carsan et al. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">2012</a>) as well as this repository using the DOI.</p>
Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology (online data)
<h1><strong>Input and output files for "Merger seismology: distinguishing massive merger products from genuine single stars using asteroseismology" (Henneco et al. 2024b)</strong></h1> <p>This repository contains the MESA and GYRE input files required to reproduce the models used in Henneco et al. (2024b), as well as some of the output files.</p> <p><strong>[MESA version]</strong><br>MESA r12778<br>MESA SDK 20.3.2</p> <p><strong>[GYRE version]</strong><br>GYRE 7.0<br>MESA SDK 22.6.1</p> <p> </p> <h2><strong>input_files</strong></h2> <p>This directory contains the template input files for the MESA and GYRE models.</p> <h3><strong>gyre</strong></h3> <p>- gyre_nonrot_template.in: GYRE inlist for computations without rotation<br>- gyre_rot_template.in: GYRE inlist for computations, including rotation using the TAR<br>- gyre_rot_pert_template: GYRE inlist for computations including rotation using the perturbative approach</p> <h3><strong>mesa</strong></h3> <p>- <strong>genuine_single</strong>: MESA work directory for genuine single stars<br>- <strong>merger_product</strong>: MESA work directory for merger products via the fast accretion method<br>- <strong>zams_z0142_y2703.data</strong>: ZAMS models used to start all MESA computations from</p> <h2> </h2> <h2><strong>output</strong></h2> <h3><strong>mesa</strong></h3> <p>In this directory, we provide the MESA history and profile (GYRE format only) output for the MESA models used in our work.<br>The more detailed regular profile files are left out because of storage constraints, but these can be transferred upon reasonable request.</p> <p>- mXX_plus_mYY_at_rZZ: XX + YY Msol merger product model where the fast accretion method was invoked when the HG star had a radius of ZZ Rsol<br>- mXX: genuine single-star model of XX Msol</p> <h3><strong>gyre</strong></h3> <p>This directory contains the GYRE summary files and input files (with the frequency ranges specific to these models). The detail files are left out because of storage constraints, but these can be transferred upon reasonable request. </p> <p><strong>[suffixes]</strong><br>NAD: nonadiabatic computations<br>pmodes: computations in frequency ranges appropriate for pressure modes<br>Om20_PERT: computations including rotation (20% of critical) using the perturbative approach<br>Om20_TAR: computations including rotation (20% of critical) using the TAR</p> <p>Except for the computations in `m6.0_plus_m2.4_at_r9.0`, the GYRE computations have been made only for a specific MESA profile (the profile at the time when the models were seismically compared).<br>These are:</p> <p>-<strong> m6.0_plus_m2.4_at_r9.0</strong>: profile 38<br>- <strong>m7.8</strong>: profile 14<br>- <strong>m9.0_plus_m6.3_at_r10.4</strong>: profile 62<br>- <strong>m13.6</strong>: profile 16</p> <p>For the `m6.0_plus_m2.4_at_r9.0` model, GYRE computations have been made for profiles 27 -- 52 (see Section 4.2).</p>
Dataset of FEUTURE Online Paper No. 10 "Knowledge Cohesion in European Regions: Convergence and Cohesion with Turkey" - Data on PhD
<p>The dataset of the paper on "Flow of Knowledge" consists of the following variables: </p> <p>- Doctorate holders by region of doctoral award 2009</p> <p>- Mobility intentions of doctorate holders by intended region of destination in year 2009</p> <p>- Principal job of employed doctorate holders by occupation and field of science, 2009 </p> <p>- Doctorate holders' satisfaction level on their principal job by selected title, 2009</p> <p>- Recent doctorate recipients' satisfaction level on their principal job by selected title, 2009</p> <p>- Doctorate holders by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Employed doctorate holders : perception regarding their job qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by employment situation and sex, 2009</p> <p>- Doctorate recepients's average and median gross annual earnings by sector of employed and sex, 2009</p> <p>- Recent doctorate recipients' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders by age group and sex, 2009</p> <p>- Employed doctotare holders by employment situation, type of contract, working time in current employment and sex, 2009</p> <p>- Proportion of recent(**) doctorate recipients by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p> <p>- Employment situation of doctorate holders by sex and year of doctorate award, 2009</p> <p>- Recent doctorate recipients by employment situation and sex, 2009</p> <p>- Doctorate recepints' avarage and median age at graduation and gross and net time to compilation their qualification by field of doctorate degree, 2009</p> <p>- Doctorate holders who moved out Turkey for a period of at least 3 months between January 2000 and December 2009(*)</p> <p>- Principal job of employed recent doctorate recipients by occupation, 2009</p> <p>- Reasons of mobility intentions of doctorate holders in the next 12 months</p> <p>- Proportion of doctorate holders by field of doctorate degree and primary(*) source of funding during completion doctorate, 2009 </p>
Log files data from online store
<p>As the number of online stores as well as buyers are increasing rapidly, researchers are working on understanding and improving the performance of online stores by studying customer behavior, interests, engagement etc., along with the technical aspects of online stores. This however requires access to log files of real-world. With this objective in mind, we have prepared and made publicly available high-frequency data-set containing one month of log files from an actual and popular Polish online store. This data-set can provide insights to user behavior as well as performance of the online store.</p>
Log files data from online store
<p>As the number of online stores as well as buyers are increasing rapidly, researchers are working on understanding and improving the performance of online stores by studying customer behavior, interests, engagement etc., along with the technical aspects of online stores. This however requires access to log files of real-world. With this objective in mind, we have prepared and made publicly available high-frequency data-set containing one month of log files from an actual and popular Polish online store. This data-set can provide insights to user behavior as well as performance of the online store.</p>
Experimental online quantum dots charge autotuning using neural networks - Output data
<p>Outputs of the model training and the online autotuning experiments presented in the paper: "Experimental online quantum dots charge autotuning using neural networks".</p> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</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.