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Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p> <p><strong>Study Context, Location and Participants</strong><br>To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's.<br>The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons.</p> <p><strong>Data Collection</strong><br>During the data collection process, session and participant details were manually recorded by the administrator. <br>Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded. <br>Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. <br>To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. <br>The assessment tool automatically tracked all user interaction within the platform.<br>All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021). <br>Data collection was integrated into a validation module of the app. </p> <p><strong>Data Features</strong><br>The dataset comprises the following files:</p> <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> <p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p> <p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p> <p><strong>REFERENCES</strong></p> <p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p> <p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p> <p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p> <p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p> <p> </p>
Explicit FE simulation results for orthopedic screw-bone interaction, for different screw geometries and bone quality
<p>The dataset disclosed herein was employed to train artificial neural network surrogate models, specifically for tasks related to screw optimization. Please read "_readMe.txt" before using.</p>
S1000 corpus, large-scale tagging results and other supplementary files
<p>Data associated with the S1000 corpus</p><p>The tagger software for which the dictionary files in <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/tagger-organisms-dictionary-S1000.tar.gz">tagger-organisms-dictionary-S1000.tar.gz </a>can be used with can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a></p><p>The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/s1000-corpus-annotation-guidelines/</a></p><p>The S1000 corpus split in training, development and test sets in BRAT format can be found in <a href="https://zenodo.org/api/records/10285825/files/S1000-corpus.tar.gz">S1000-corpus.tar.gz</a><a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000-corpus.tar.gz?versionId=ac7ce430-c265-49bb-8c8f-9b5f8e271cbe"> </a>and in CoNLL format here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/s1000-conll.tar.gz">s1000-conll.tar.gz</a></p><p>The tagging results of Jensenlab tagger for the S1000 test set are here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000-jensenlab-tagger.tar.gz?versionId=d8d9c9f5-ee3b-4738-aefa-a4a95475d25d">S1000-jensenlab-tagger.tar.gz</a></p><p>The result from the large scale run in entire PubMed and PMC Open Access articles for Jensenlab tagger is provided here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/Jensenlab_tagger_large_scale_matches_with_rank.tsv.gz?versionId=48825928-9fc9-423c-8a4c-4f8994e95805">Jensenlab_tagger_large_scale_matches_with_rank.tsv.gz</a></p><p>The model used for the large scale run of the transformer-based method is here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000_Transformer_based_tagger_large_scale_model.tar.gz?versionId=8e974f64-9abc-4449-a377-e3f97e91d612">S1000_Transformer_based_tagger_large_scale_model.tar.gz</a> and the results from the large scale tagging here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/Transformer_based_tagger_large_scale_matches_with_rank.tsv.zip?versionId=dc21a6ba-9763-4130-9f02-0341a885c692">Transformer_based_tagger_large_scale_matches_with_rank.tsv.zip</a></p>
Raw data for the submitted manuscript entitled "Prospective Scenarios for Addressing the Agricultural Plastic Waste Issue: Results of a Territorial Analysis"
<p><span>Agricultural activities have been positively affected by the use of plastic products, but this has resulted in the production of plastic waste and led to an increase in environmental pollution. </span><span>This file concerns plastic waste indices to different crop types and plastic products allowed quantifying and georeferencing actual plastic waste production. Two improved scenarios were considered, the first consisted of extending the lifespan of some plastics, and the second entailed the introduction of some biodegradable alternatives. </span></p>
User Stories made by Users Workshop Analysis Results
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and the resulting data set has been published on Zenodo:</p> <p>Sell, Johann, & John, Elias. (2020). User Stories made by Users Workshop Data Set (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898358</p> <p>A qualitative analyzes has been conducted covering four iterations of coding. This data set shows the results of last iteration and the resulting insights are referenced by a research paper that is currently under review.</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, and to (b) investigate other interesting research questions.</p>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p><p><strong>Study Context, Location and Participants</strong><br>To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023.<br>The study was conducted in Switzerland, specifically within the Ticino canton.<br>The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). </p><p><strong>Data Collection</strong><br>Data collection was integrated into a validation module of the app. <br>Sessions required manual input for details like date, canton, and school information. <br>Students' details, anonymised for privacy, encompassed their gender and date of birth. <br>Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.</p><p><strong>Data Features</strong><br>The dataset comprises the following files:</p><ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul><p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p><p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p><p> </p><p><strong>REFERENCES</strong></p><p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p><p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p><p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p><p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p>
Trabecular bone – screw interaction. Micro-CT models and experimental push-in results.
<p>The dataset disclosed herein was employed to build the screw-bone interaction models, specifically for tasks related to screw push-in simulation.</p>
Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool
<p>This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu</p>
Experimental Results for the AERO 5G project (Fed4FIRE+)
<p>The corresponding results refer to the experiments conducted during the life of the Fed4FIRE+ project entitled: "AERO 5G (Augmented Reality Tour Guide Architecture for 5G)". The public availability of the results aim to help future experimenters and researchers to obtain some intuition with regard to the benefits of 5G for content-based, bandwitdh consuming, MAR applications.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Short Description of the Experiments: All values have been rounded to two decimal places. Our team has conducted ten experimental runs for each of the following experimental scenarios.</p> <p>o <strong>4G SDR srsLTE-to-AWS</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o<strong> 4G SDR srsLTE-to-IMEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o <strong>4G SDR srsLTE-to-Iris-MEC</strong>: A 4G SDR srsLTE network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure at the Iris testbed that hosts the AR video content. Although MEC is considered to be a 5G technology, our aim in this scenario is to explore the benefit of deploying edge storage nodes in wireless mobile telecommunication technologies in general. For this purpose, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests.</p> <p>o <strong>Commercial Three.ie 4G-to-AWS</strong>: A Commercial 4G network deployed by the Three.ie mobile operator in Ireland, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node that hosts the AR video content. Even though a MEC storage node could not be deployed in this scenario, our intention is to estimate the benefit of deploying edge storage nodes empirically by consulting the results concluded for the 4G LTE-to-MEC scenario.</p> <p>o <strong>2.4GHz Wi-Fi-to-IMEC</strong>: A 2.4GHz Wi-Fi (802.11 n) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts 4K AR video. This scenario serves us as a proof-of-concept that a 2.4GHz Wi-Fi network is not capable of supporting the delivery of high quality 4K AR content in areas where there are a lot of 2.4GHz Wi-Fi networks.</p> <p>o <strong>5GHz Wi-Fi-to-AWS</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and an Amazon EC2 node at Amazon Cloud (AWS) that hosts the AR video content.</p> <p>o <strong>5GHz Wi-Fi-to-IMEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a bare metal machine at Fed4FIRE+ IMEC's VirtualWall that hosts the AR video content.</p> <p>o<strong> 5GHz Wi-Fi-to-Iris-MEC</strong>: A 5GHz Wi-Fi (802.11 ac) network deployed over the Fed4FIRE+ Iris testbed, between a Xiaomi Mi Mix 2S handset and a MEC storage node located at the edge of the network infrastructure that hosts the AR video content. Similar to the 4G LTE-to-MEC scenario, we assume that the video content has been stored at the MEC storage node a priori to the end-user's requests. </p>
ERA-5 reanalysis results interpolated onto the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>ERA-5 fields at 1-hour temporal and grid size of 0.25° x 0.25° (0.5° x 0.5° for wave variables) have been downloaded from <a href="https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels">https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels</a>.</p> <p>The data are interpolated using two methods:</p> <p>'nearest': the value of the nearest ERA-5 grid cell is use;</p> <p>'linear': the values from the nearest grid cells in space and time are linearly interpolated to the [date_time, latitude, longitude] coordinate of the ship</p> <p>providing a number of atmospheric, land and oceanic climate variables interpolated along the five-minute cruise track.</p> <p>The data repository can be checked out at: <a href="https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track">https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track</a></p> <p><strong>Dataset contents</strong></p> <ul> <li>era5-on-cruise-track-5min-legs0-4-linear.csv, data file, comma-separated values</li> <li>era5-on-cruise-track-5min-legs0-4-nearest.csv, data file, comma-separated values</li> <li>interpolate-to-shiptrack.py, processing script, text/x-python</li> <li>download-ecmwf.ipynb, processing script, application/x-ipynb+json</li> <li>ecwmf_interpolate.zip, processing scripts, zip file</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This interpolation of the ERA-5 reanalysis output to the five-minute averaged cruise track and velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Results of the color picking survey
<p>This datasets summarizes the results of the colorpicking survey that we conducted at EPFL using the colorjeopardy app (<a href="https://doi.org/10.5281/zenodo.3831841">10.5281/zenodo.3831841</a>) in spring 2020. </p> <ul> <li>color_string: is the color name from the CSD that was presented the survey participants</li> <li>hex: is the hex code of the color the survey participants picked</li> <li>starttime: is the time the participants started picking the color</li> <li>time_stamp: is the time the participants submitted the pick </li> </ul>
Leaf Vein Network CNN Results
<p>Results for leaf vein networks extracted using the LeafVeinCNN software package. The original image data set is available from Blonder et al. (2019) <a href="https://doi.org/10.1002/ecy.2844">https://doi.org/10.1002/ecy.2844</a>. The LeafVeinCNN software used in the analysis is available at <a href="https://doi.org/10.5281/zenodo.4007731"> </a><a href="https://doi.org/10.5281/zenodo.4007730">https://doi.org/10.5281/zenodo.4007730</a></p> <ul> <li>The Results_xxx.zip files contain all the Excel results spreadsheets separated by the code for each field site.</li> <li>results.xls provides a summary of all the network metrics for each file that was analysable</li> <li>Results_figures.pdf provides a summary image of the processing steps and results for each leaf segment</li> <li>Network_images.pdf contains a colour-coded image of each network superimposed on the leaf segment</li> <li>HLD_plots shows the binary tree following Hierarchical Network Decomposition</li> <li>PR_results.zip contains the Excel spreadsheets for evaluation of different enhancement methods for each leaf segment.</li> <li>PR_summary.xls provides a summary of the performance of each enhancement method.</li> <li>PR_F1_images.pdf and PR_FBeta2_images.pdf show the pixel classification for each enhancement and segmentation method compared to the manual ground-truth using two different optimum criteria (F1 and FBeta2).</li> <li>PR_fullwidth_plots show the full Precision-Recall plots for the full-width binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_skeleton_plots show the full Precision-Recall plots for the skeletonised binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_threshold_plots.pdf show how a set of network metrics vary with the segmentation threshold for each enhancement method.</li> </ul>
Sonic Kayak feedback survey results
<p>This data set is part of the Sonic Kayak project https://fo.am/activities/kayaks/</p> <p>An anonymous survey was performed online in August/September 2020, to gather people's opinions on the Sonic Kayak project, as we were not able to take people out to try the kayaks in person. A video explainer was provided (https://www.youtube.com/watch?v=puLXKj1AVAk) followed by optional prompting questions. The data presented is in its raw form.</p>
AIOps Systematic Mapping Study - Results
<p>Results from our work "A Systematic Mapping Study in AIOps" (https://arxiv.org/abs/2012.09108).</p> <p>The file 'papers.csv' contains the complete list of AIOps papers identified, with corresponding metadata (author, year, citations, venue) and indexing annotations (macro-area, category, data sources, etc.) as columns. This file enables to explore and reproduce our results from scratch. For a quick and interactive exploration of the results without coding, you can check out the same dataset on Exploratory.io (https://exploratory.io/project/EDK0DNx1Qe/AIOps___Mapping_Study_pXe9pUn2). </p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening Test Results
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the results of the listening test.</p>
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Evaluation datasets and results for the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays"
<p>Event-logs and Business Process Simulation Models used in the experimentation of the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays", where the '<em>inputs</em>' folder contains all the files used as input, and the '<em>output</em>' folder the results of the evaluation.</p> <p> </p> <p><em><strong>Inputs</strong></em>: event-logs, BPS models, and simulation parameters used as input in the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: real-life event logs, corresponding to two disjoint subsets of traces from an Academic Credentials' process, and the BPIC 2012 and BPIC 2017 event logs (filtered as explained in the paper), and the BPS model (plus simulation parameters) used as input for each dataset in the presented approach.</li> <li><em><strong>Synthetic</strong></em>: simulated event-logs and corresponding BPS models (plus simulation parameters) for four different processes with 0, 1, 3 and 5 timer events.</li> </ul> <p><em><strong>Outputs</strong></em>: results of the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: results corresponding to the evaluation with real-life event logs. Each of the folders is composed by the original and the enhanced BPS models, 10 event logs simulated with each of them, two folders with the best iteration of the two hyperparameter optimization processes, and the values for the injected timers in each case. In addition, a CSV file with the EMD metrics (cycle time and absolute hour event distribution) for each dataset is provided.</li> <li><em><strong>Synthetic</strong></em>: results corresponding to the simulated event-logs. <ul> <li>Before-After: BPS models and discovered timer events for the four synthetic processes, with five timers placed before and after different activity instances.</li> <li>Complete: BPS models and quality measures (precision, recall, and SMAPE of the discovered timers) for the four synthetic processes with zero, one, three, and five timer events.</li> <li>Individual: event logs enhanced with the discovered extraneous delay for each activity instance, for the four synthetic processes with zero, one, three, and five timer events; and SMAPE of the estimations.</li> </ul> </li> </ul>
Results of Survey on Playertypes by Gamification User Types Hexad Framework in Higher Education
<p>Survey on playertypes via the validated quesitonaire published in Krath, J., von Korflesch, H.F.O. (2021). Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale. In: Fang, X. (eds) HCI in Games: Experience Design and Game Mechanics. HCII 2021. Lecture Notes in Computer Science(), vol 12789. Springer, Cham. https://doi.org/10.1007/978-3-030-77277-2_18</p> <p>Between 25.01.23 and 08.02.23 students of the University of Lübeck, Germany were invited to fill out an online questionnaire. The acquisition was done by sending an email to the students. No incentive was offered for participation, except to find out at one's own expression at the end of the survey. In addition to the validated questions, this also included questions about gender and study area. All participants agreed to anonymous data collection and publication. Participants were also asked to confirm that they were completing the survey for the first time, otherwise the return was removed from the result set. </p> <p>The result set is formatted as CSV. Questions and identifiers of the data are shown in the first line.</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.