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.
47,155
datasets available to search
ShareScore release 0.9.0
Dataset results
47,155 results for “evaluations”
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</p>
An Empirical Evaluation of the "Cognitive Complexity" Measure as a Predictor of Code Understandability
<p>The paper provides an evaluation of the “Cognitive Complexity” measure as an indicator of code understandability.<br> The evaluation is performed via an empirical study.<br> ""Cognitive Complexity" is compared with traditional code measure, like LoC and McCabe's complexity.</p> <p>The data used in the empirical study are provided.</p>
Post-remediation evaluation of contaminated site using geophysical methods: photos
<p>Photos of the research area.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods” </p>
Post-remediation evaluation of contaminated site using geophysical methods: ERT
<p>The ERT measurements (7 profiles: M1-M7) were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629
<p>In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Post-remediation evaluation of contaminated site using geophysical methods: Digital Elevation Model Olkusz (Poland) 20220629
<p>The Digital Elevation Model is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal<br> length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is a DEM with a 51.1 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326). </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</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>
General Acyclicity and Cyclicity Notions for the Disjunctive Skolem Chase - Evaluation Material
<p>In this archive, we provide the (already normalized and translated) rule sets that we used for the evaluation<br> of MFA, DMFA, DMFA2, MFC, and DMFC in our paper "General Acyclicity and Cyclicity Notions for the Disjunctive Skolem Chase" at AAAI 2023.We also provide the raw result files and some basic scripts that we used<br> to produce and count the results.<br> Due to licensing restrictions we are not allowed to include all of our evaluation material.<br> <br> Please refer to the provided README.md for more information.</p>
Ecotoxicity test results for the paper entitled "Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater"
<p>Within the frame of the ELECTRA No. 826244 H2020 project, a sequential reductive/oxidative bioelectrochemical process at a laboratory scale was tested with real groundwater from a contaminated site in Northern Italy for chlorinated aliphatic hydrocarbons (CAHs) removal. The changes in groundwater ecotoxicity were followed by a complex ecotoxicity test battery including testorganisms from various trophic levels. The datasets (supplementary to Chapter 3.5) for this publication are presented here. Referred publication: Edoardo Dell'Armi, Marco Zeppilli, Maria Letizia Di Franca, Bruna Matturro, Viktória Feigl, Mónika Molnár, Zsófia Berkl, Imre Németh, Hafsa Yaqoubi, Simona Rossetti, Marco Petrangeli Papini, Mauro Majone, Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater, Journal of Water Process Engineering, 49, 2022, 103101, https://doi.org/10.1016/j.jwpe.2022.103101.</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>
Evaluation of the Purity of Magnesium Hydroxide Recovered from Saltwork Bitterns
<p>Magnesium has been listed among the 30 critical raw materials by the European Union. In recent years, many green and sustainable alternative Mg<sup>2+</sup> sources have been sought to satisfy the EU’s demand and to avoid mineral ore consumption. In this context, saltwork bitterns, the by-products of solar sea salt production, have attracted much attention thanks to their high Mg<sup>2+</sup> concentrations (up to 80 g/L) and low Ca<sup>2+</sup> and bicarbonate contents (<0.5 g/L). Although investigations on Mg<sup>2+</sup> extraction from bitterns in the form of Mg(OH)<sub>2</sub>(s) have already been performed, product purity has never been properly addressed. Mg(OH)<sub>2</sub>(s) is a chemical compound of great interest and extensive utility in numerous industrial applications only if the powder’s purity is >95% (w/w). This work presents a comprehensive experimental effort of reactive precipitation tests with NaOH solutions at stoichiometric and over-stoichiometric concentrations to: (i) assess the technical feasibility of Mg<sup>2+</sup> recovery from real bitterns collected in saltworks of the Trapani district (Italy) and, (ii) for the first time, conduct an extensive purity investigation of the precipitated magnesium hydroxide powders as brucite. This experimental investigation demonstrates the possibility of extracting highly valuable compounds from saltwork bittern waste, embracing the water valorization and resource recovery approach.</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>
WikiCausal Corpus for Evaluation of Causal Knowledge Graph Construction
<p>Documentation on the data format and how it can be used can be found on: <a href="https://github.com/IBM/wikicausal">https://github.com/IBM/wikicausal</a> as well as our paper:</p> <pre><code>@unpublished{, author = {Oktie Hassanzadeh and Mark Feblowitz}, title = {{WikiCausal}: Corpus and Evaluation Framework for Causal Knowledge Graph Construction}, year = {2023}, doi = {10.5281/zenodo.7897996} }</code></pre> <pre>Corpus derived from Wikipedia and Wikidata. Refer to Wikipedia and Wikidata <a href="https://en.wikipedia.org/wiki/Wikipedia:Copyrights">license and terms of use</a> for more details:</pre> <ul> <li><strong>Permission is granted</strong> to copy, distribute and/or modify Wikipedia's text under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License and, <em>unless otherwise noted</em>, the GNU Free Documentation License, unversioned, with no invariant sections, front-cover texts, or back-cover texts.</li> <li>A copy of the Creative Commons Attribution-ShareAlike 3.0 Unported License is included in the section entitled "<a href="https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License">Wikipedia:Text of Creative Commons Attribution-ShareAlike 3.0 Unported License</a>"</li> <li>A copy of the GNU Free Documentation License is included in the section entitled "<a href="https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License">GNU Free Documentation License</a>".</li> <li>Content on Wikipedia is covered by <a href="https://en.wikipedia.org/wiki/Wikipedia:General_disclaimer">disclaimers</a>.</li> </ul> <pre>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</pre>
Parameters for Statistical Evaluation of Time to Fixate Efectiveness for Assessment of Fitness to Drive
<p>This repository contains a table with parameters (including time to fixate - TTF parameter) and an R script for statistical analysis. This is supplementary material for the paper titled "<a href="https://doi.org/10.3758/s13428-023-02177-3">Effectiveness of a Time to Fixate for Fitness to Drive Evaluation in Neurological Patients</a>" and authored by Nadica Miljković and Jaka Sodnik (published in <a href="https://www.springer.com/journal/13428">Behavior Research Methods</a> and previously shared on <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.08942.pdf">arXiv</a>).</p> <p>TTF parameter was calculated in overall 56 patients during selected scenario with pedestrian collision in a driving simulator produced by <a href="https://www.nervtech.com/">Nervtech</a>. Together with other parameters, ST parameters are stored in <a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, while R programming code for statistical analysis of all parameters is placed in <a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, table with parameters, csv (comma-separated values) format</li> <li><a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>, code in R programming language for statistical analysis</li> </ol> <p><strong>Table with parameters has the following structure</strong></p> <ol> <li>column - no which is ordinary number in consecutive order from 1 to 56</li> <li>column - id presents an internal patient's id</li> <li>column - ttf presents TTF parameter in ms</li> <li>column - fitness presents a categorical variable and can be either fit-, unfit-, or conditionally-fit-to-drive (cond fit)</li> <li>column - speed at the collision onset in km/h</li> <li>column - ttc presents time-to-collision in s</li> <li>column - manual_correction is categorical variable: 0 means that no manual correction was required for ST calculation, while 1 means that manual correction was required</li> <li>column - igd presents initial gaze distance in pixels</li> <li>column - R1 presents the first measurement of perception response time (PRT)</li> <li>column - R2 presents the second measurement of PRT</li> <li>column - R3 presents the third measurement of PRT</li> </ol> <p>Missing data are presented with NA (Not Available).</p> <p>NOTE: Python code for ST calculation and sample eye tracker video are available on GitHub repository <a href="https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos">https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos</a> under GNU GPL license and released on Zenodo with doi (<a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a>).</p> <p>If you find these parameters and R code useful for your own research and teaching class, please cite the following references:</p> <ol> <li>Miljković, N., & Sodnik, J. (2023). NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos: v2. [Software code], Zenodo. <a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a></li> <li>Miljković, N., & Sodnik, J. (2023). Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients. Behavior Research Methods. <a href="https://doi.org/10.3758/s13428-023-02177-3">https://doi.org/10.3758/s13428-023-02177-3</a></li> <li>Motnikar, L., Stojmenova, K., Štaba, U. Č., Klun, T., Robida, K. R., & Sodnik, J. (2020). Exploring driving characteristics of fit-and unfit-to-drive neurological patients: A driving simulator study. Traffic Injury Prevention, 21(6), 359-364. <a href="https://doi.org/10.1080/15389588.2020.1764547">https://doi.org/10.1080/15389588.2020.1764547</a></li> </ol> <p><strong>Acknowledgements</strong></p> <p>J.S. kindly acknowledges University Rehabilitation Institute Soča employees and the Nervtech team. Authors gratefully appreciate the support from Nenad B. Popović, PhD from University of Belgrade – School of Electrical Engineering for his valuable assistance in design of illustrations and for provided feedback for the initial manuscript structure. Also, both Authors thank Nebojša Jovanović, MSc from University of Belgrade - School of Electrical Engineering for his kind contribution to earlier stages of the project, especially for his work on developing Python code to capture time to fixate parameter. Last but not least, we are very thankful to Damjan Krstajić, founder and director of the Research Centre for Cheminformatics for his precious advices on statistical analysis in a retrospective study and to student Gregor Kovač from Faculty of Electrical Engineering, University of Ljubljana for his diligent work on YOLO application in driving simulation.</p>
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>
Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination - Evaluation Material
<p>In this archive, we provide the (already normalized and translated) rule sets that we used for the evaluation<br> of RMFA2, DRPC, and RPC in our paper "Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination" at KR 2023. We also provide the raw result files and some basic scripts that we used<br> to produce and count the results.<br> <br> Please refer to the provided README.md for more information.</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.