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470 results for “Evaluation dataset”
Datasets for "BB correction" algorithm evaluation and analysis
<p>The datasets contain the raw and processed data relevant to the "BB (black body) correction" method, an algorithm to correct for scattering and systematic bias in quantitative neutron imaging from experimental measurements. Five CT datasets imaging various hydrogenous and non hydrogenous materials are uploaded. The raw data folders for each dataset contains the sample projections over the tomographic range as well as the image used for referencing: the dark current, the open beam files and the additional "BB images", obtained with an interposed grid of neutron absorbers (BBs) from which scattering and bias components are estimated. The processed folders contain the CT reconstruction with and without the BB correction. All processing was done with the MuhRec open source software (https://zenodo.org/record/1311698#.W30exxixVtg)</p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
Simulated dataset (Almeida et al., 2018 - GigaScience) treated with various clustering programs to evaluate ReClustOR efficiency and constitency
<p>ReClustOR is a novel clustering method that overcomes some of the problems associated with classical ‘<em>heuristic’</em>clustering methods and consequently increases the stability and quality of the reconstructed OTUs. Moreover, the OTUs database defined with ReClustOR can be used as reference(s) with gradual enrichment of it, with new studies and samples. In this way, huge datasets like<em> </em>the Earth Microbiome Project can be easily used as references for smaller projects, thereby increasing the quality of comparisons between studies and datasets</p> <p>Here, we propose a new approach called ReClustOR (for RE-CLUSTering method using an Open-Reference approach) to improve OTU consistency (see https://doi.org/10.5281/zenodo.2597402). This new strategy combines two of the previously-described clustering methods. Firstly, a classical clustering<em> </em>method (<em>e.g. </em>SWARM, or VSEARCH) is used to define OTU centroids and create a reference database. Secondly, a closed- or open-reference method (depending on the user’s choice) is computed for all reads which are not considered as OTU centroids. Contrary to the classical clustering methods, each read is compared to all centroids using a distance-based greedy clustering technique (Edgar, 2010; He et al., 2015), and then assigned to the nearest one, thereby fixing the erroneous assignments of reads to OTUs.</p> <p>To highlight the improvements provided by ReClustOR in describing microbial diversity in terms of ecological diversity metrics (<em>e.g. </em>richness, OTU composition, Shannon, 1/Simpson) and taxonomic composition, a simulated dataset was subjected to: (i) ESV definition, (ii) multiple conventional <em>de novo</em> methods (<em>i.e.</em> a homemade <em>de novo </em>clustering close to CRUNCHCLUST, VSEARCH and SWARM), and (iii) ReClustOR computation. This dataset is a simulated one (Almeida et al., 2018), containing a diverse set of <em>genera</em> commonly found in three ecosystems different ecosystems: human gut, ocean and soil. The clustering methods were compared for: (i) their ability to describe microbial richness, (ii) the congruence between OTU assignments and sequences taxonomy, (iii) the robustness of each defined OTU, and (iv) their ability to efficiently describe the microbial community based on OTU composition.</p> <p>Here, the simulated dataset (00_Raw_data) and all steps of analysis are available to resue them to test ReClustOR, and also to have a better understanding of files and data produced by this program. More details are available in the Tree_of_data.tree file.</p>
DATASET Clinical and Radiographic Evaluation Data of Autotransplantation using 3D Replicas
<p>This dataset repository contains detailed data and analysis scripts related to a research study on autotransplantation using 3D replicas. The study aims to evaluate clinical outcomes and radiographic changes over time in dental autotransplantation cases. The dataset includes CSV files containing clinical data, detailed radiographic evaluation results, and HTML files documenting analysis procedures and findings.</p>
Dataset for the evaluation of Supercritical Fluid Chromatography Polar Stationary Phases with OH moieties
<p>Raw data used for the evaluation of supercritical fluid chromatography stationary phases with OH moieties published in article "Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with OH moieties" in Analytical Chemistry, 2024. Data set contains: (i) chromatograms of 107 analytes measured on silica, hybrid silica, and diol column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet). </p>
UnientrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers
<p>Our work focuses on providing a comprehensive dataset and benchmarks for evaluating gene ontology annotations using a unified system of Entrez Gene Identifiers.</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Throwbot - Dataset and evaluation of a thrown table tennis egg
<p>Dataset, videos and MATLAB live script for the evaluation of the trajectory of a thrown table tennis egg</p>
SIMPATICO Second Evaluation Galicia Dataset v1.0
<p><strong>SIMPATICO logs for the user evaluation of Galicia in project iteration 2</strong></p> <p>The current package contains the Interaction LOG data captured in the Galicia evaluation of the results of H2020 project SIMPATICO that were undertaken between September 24th 2018 and October 15th 2018. This contains a total 290 user tests that were conducted. The data is exported from the Elasticsearch instance that was used to log all of the interaction data. The data model for this can be found in project deliverable "D3.3 Advanced Methods And Tools For User Interaction Automation". For more information about the setup for conducting the tests and the results achieved please consult project deliverable "D6.6 SIMPATICO Evaluation Report v2". All project deliverables, except where noted, are public and are available at Zenodo community reachable at https://zenodo.org/communities/h2020-simpatico-692819.</p> <p>The following caveats need to be highlighted for this data set:<br> - Due to limitations in the dumping mechanisms in the Elasticsearch it has been divided in two different sub-logs (24th September to 7th October, 8th October to 15th November). All of the registers in the data set are nonetheless identified by date and time so the data set as a whole is a continuous recount of the captured data.</p> <p>- The format is JSON (Javascript objects) as provided by Elasticsearch. </p> <p>- Data is completely anonymized: no traces of personal data for any of the 374 participants can be found in this file. Individual user logs can be traced from the "userID" field that is stored, containing either a unique identifier that is backed to a logged-in user (in the cases in which just a number is stored) or a user who is interacting but has not yet logged (this is signified by the "no_user_logged_" prefix, followed by another unique identifier that can trace users interacting before login).</p> <p><strong>Change Log</strong></p> <p>v1.0 - 2018-12-13 - Initial release</p> <p>Disclaimer: this data set was created by the consortium of project SIMPATICO (GA 692819, http://www.simpatico-project.eu). The data is provided here as-is with no liability due to the authors for its completeness or validity. Recipients of this data can freely use it in any form following due contact with the project administration at info@simpatico-project.eu).</p> <p>(c) SIMPATICO - 692819 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 692819.</p>
Dataset: Comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research.
<p>Supplementary material for a comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research. We conducted a relevance evaluation with 6 users over 19 search questions in two search interfaces.</p> <p>The users provided up to five search questions and relevant keywords from their research background. We setup a dataset search over a corpus of ~92,000 randomly selected metadata files from GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>). For each of their own search queries, the users got two result sets presented. The first one displayed results obtained from a keyword search. The second panel contained dataset results from a prototypical semantic search. Instead of results with exact mentions of the query terms, the semantic search also presented related results with synonyms and more specific terms or terms obtained from concept nodes of a higher hierarchy level.</p> <p>Each user rated the relevance of his/her own search queries on a 7-point Likert scale for both search results.<br> In addition, users also assessed the expanded keywords for each question.</p> <p>More information can be found in our publication:</p> <p>Löffler, F. and Klan, F. (2016): Does Term Expansion Matter for the Retrieval of Biodiversity Data? in Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16), co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016),2016, <a href="http://ceur-ws.org/Vol-1695/paper2.pdf">http://ceur-ws.org/Vol-1695/paper2.pdf</a></p> <p> </p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Dataset for Appearance evaluation of digital materials in material jetting
<p>Dataset for Appearance evaluation of digital materials in material jetting: Including dataset for gloss, haze, scattering, specular BRDF, reflectance, and transmittance</p>
Dataset for Evaluating Abstractive Summaries of Crisis-Related Social Media
<p>The dataset created for evaluation of summaries generated from social media posted during five natural disasters.</p> <p>The dataset contains:</p> <ul> <li> <p>ground truth reports created by human assessor based on ERCC Echo Flash reports (5 events);</p> </li> <li> <p>summaries generated by extractive and abstractive state-of-the-art with manual annotation of category-relevant and crisis-relevant claim.</p> </li> </ul> <p>The dataset with annotated social media postings — <a href="https://doi.org/10.5281/zenodo.7714014">link</a></p> <p>The full description of used summarization methods, sources of data and evaluation metrics available in the paper — <a href="https://dl.acm.org/doi/abs/10.1145/3511095.3531279">link</a></p>
Bengali Identity Bias Evaluation Dataset (BIBED)
<p>Critical studies found NLP systems to bias based on gender and racial identities. However, few studies focused on identities defined by cultural factors like religion and nationality. Compared to English, such research efforts are even further limited in major languages like Bengali due to the unavailability of labeled datasets. Our paper (see the reference) describes a process for developing a bias evaluation dataset highlighting cultural influences on identity. We also provide this Bengali dataset as an artifact outcome that can contribute to future critical research.</p> <p>If you find this dataset useful, please cite the associated paper:</p> <p>Das, D., Guha, S., & Semaan, B. (2023, May). Toward Cultural Bias Evaluation Datasets: The Case of Bengali Gender, Religious, and National Identity. In <em>Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)</em> (pp. 68-83).</p> <p>BibTeX:</p> <pre>@inproceedings{das-etal-2023-toward, title = "Toward Cultural Bias Evaluation Datasets: The Case of {B}engali Gender, Religious, and National Identity", author = "Das, Dipto and Guha, Shion and Semaan, Bryan", booktitle = "Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.c3nlp-1.8", pages = "68--83", }</pre>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform
<p>Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform<br> =======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7912941</p> <p><br> • The corresponding article : El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p> <p><br> • License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date: May 9th 2023</p> <p>• Subject: Educational change, Scalability, Professional Development, Digital Education, Curricular<br> Reform, Primary School</p> <p>• Dataset format: CSV</p> <p>• Dataset collection: September 2018 to September 2022</p> <p>• Dataset size : < 100 kB</p> <p>• Dataset content : one excel file with detailed description below. Please note that the spreadsheet may contain missing values due to teachers either choosing not to respond to the questions or the questions not being presented at each of the training sessions. To have access to the specific survey questions please refer to the associated publication [a].</p> <p>• Abbreviations :<br> - DE : Digital Education<br> - PD : Professional Development</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p># References</p> <p>[a] El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p>
OneNet - AccessNet (Czech Demo - part e-fleet) - dataset + KPI evaluation
<p>File contains all datasets and KPI evaluation related to OneNet - AccessNet project (under Czech Demo - part e-fleet).</p>
Dataset for paper "A Close-Range Photogrammetric Surface Scanner and its Evaluation"
<p>This dataset is supplementary material to the submission “A low-cost close-range photogrammetric surface scanner”, by Panagiotis Koutlemanis, Xenophon Zabulis, Nikolaos Stivaktakis, Nikolaos Partarakis, Emmanouil Zidianakis, and Ioanna Demeridou. The dataset contains 3D reconstructions that show the results of a close-range photogrammetric approach and its implementation using a low-cost CNC device.</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</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.