Find research datasets worth reusing

opencc-by-4.0Sep 2017View details →

Information Extraction System: a case study in information extraction from the Polish Fire Service rescue reports

The mind maps of the car accident ontologies.

opencc-by-4.0Apr 2024View details →

Additional Material for Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture

This upload contains the replication package for the study "Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture" submitted for review on the ECSA 2024 Industry Track. It contains the following: Focus group-plan.pdf - The plan for the focus group discussion created by the authors. Focus group slides.pdf - The slides used during the focus group discussion. SonarQube measurement results.xlsx - Detailed results of the SonarQube analysis for each microservice in the researched system.

openlgpl-3.0-or-laterApr 2024View details →

Supplementary material for "Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study"

Supplementary material for ”Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study". The <a href="https://github.com/sonasalehian/AHC-Poroelastic-Model.git" target="_blank" rel="noopener">AHC-Poroelastic-Model</a> codes from repository is included in the AHC-Poroelastic-Model-main.zip file.

opencc-by-4.0Apr 2024View details →

Investigation on the impact of solar flares on the Martian atmospheric emissions in the dayside near-terminator region: Case studies.

Mars, Solar Flares, Martian Airglow Emissions, MAVEN, MAVEN/IUVS, EUVM, and SWEA.

Basic-ECVs for Case Studies (yearly timeseries)

Yearly timeseries (.csv) of basic-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data: <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> Acknowledgement also to: <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://web.archive.org/web/20250325064114/https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://web.archive.org/web/20250325064114/https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>

Rapid Flow-Based Synthesis of Post-Translationally Modified Peptides and Proteins: A Case Study on MYC's Transactivation Domain

Raw data for the project "Rapid Flow-Based Synthesis of Post-Translationally Modified Peptides and Proteins: A Case Study on MYC's Transactivation Domain". Manuscript and supporting information available on Chemical Science: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4SC00481G">https://doi.org/10.1039/D4SC00481G</a>

Repurposing natural gas pipelines for hydrogen: limits and options from a case study in Germany (Calculations, Data)

This dataset contains all data for Télessy, Barner and Holz (in preparation): Repurposing natural gas pipelines for hydrogen: limits and options from a case study in Germany. The Python code can be run to reproduce the calculation results, the Excel data provides additional overview of inputs

opencc-by-4.0Apr 2024View details →

Dataset: The bed and board services of crinoids to their associated fauna: a case study from the Great Reef of Toliara, SW Madagascar

This is the datasets of Carbon and Nitrogen stable isotopes values of several crinoid species and their symbionts collected in the Great Reef of Toliara (SW Madagascar). These values have been submitted in the following manuscript: "The bed and board services of crinoids to their associated fauna: a case study from the Great Reef of Toliara, SW Madagascar" submitted in the journal "Community Ecology"

Adoption of Climate-Smart Agricultural Practices and Its Impact on Crop Productivity: A Case Study of Smallholder Farmers in Nyimba District, Zambia

Open the record for dataset details and reuse information.

A green approach to achieve extended lubricity of tribological systems: car-bon nanoparticles as a case study

The videos show the testing of Nickel samples using a a custom-built ring-on-block prototype against a SAE 1045 counter body ring. The reference corresponds to the unreinforced Nickel, whereas the CNT sample corresponds to a laser-textured Ni/CNT composite with CNT-coating.  An impressive noise reduction was detected during testing the laser-textured Ni/CNT composite with CNT-coating sample. Although the noise reduction was not quantified, it is evident that a qualitatively improvement in noise pollution can be appreciated.

Modelling input data for the case study of the paper "Uncertainty-Based Market-Clearing Models: A Comparative Analysis of the Dutch, French, and German Markets".

This data package includes the modelling input data to replicate the results of the case study included in the paper "Uncertainty-Based Market-Clearing Models: A Comparative Analysis of the Dutch, French, and German Markets".  The case study models the Dutch, French and German day-ahead electricity markets, in which the existing capacities of electricity generation and upward- and downward reserve capacities are considered, in addition to 105 wind output realization scenarios for each simulation day. A detailed description of the case study is provided in the readme file. This supplementary data package includes the following files: -        Meta Data – Netherlands.xlsx: Dataset containing the meta data for the Dutch case study -        Meta Data – France.xlsx: Dataset containing the meta data for the French case study -        Meta Data – Germany.xlsx: Dataset containing the meta data for the German case study -        Readme.txt: Includes a detailed description of the data packages

opencc-by-4.0Dec 2023View details →

Supplemental material: The microbiota of ensiled forages and of bulk tank milk on dairy cattle farms in northern Sweden - a case study

Supplemental Figures.

opencc-by-4.0Feb 2024View details →

Effects of European emissions trading on the transformation of primary steelmaking: Assessment of economic and climate impacts in a case study from Germany

Supplementary Information 1: This supporting information provides information on the modeling of material and energy flows for conventional and low-carbon steelmaking in an integrated steel mill. To this end, the production activities (unit processes) are provided. Also, information on the assessment of economic and climate impacts is included. Supplementary Information 2: This supporting information provides information on the main mechanisms of the European emissions trading system. Thereby, a focus is laid on regulations for the free allocation of EU allowances toward companies from the sectors with a high risk of carbon leakage. Additionally, the assessment and optimization models for designing favorable transformation pathways of integrated steel mills are provided. Also, the main assumptions and complementary results are provided.

Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'

Three Zenodo repositories are linked to the preprint Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area  (Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>): <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> This repository contains the outputs from fitted models across the cross-validation scenarios. In the folder Ouputs_cross_validation, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to Bombycilla garrulus; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the Results/Fitted_models folder and run the Models_Outputs.R script located in the Results folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a> Note: These data have been separated due to memory requirements (23.14GB).

Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'

Three Zenodo repositories are linked to the preprint Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area  (Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>): <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> This repository contains data, scripts, and code. It is organized into two main directories: Materials and Methods, and Results. <h2>Materials and Methods</h2> The raw data can be accessed in the Materials_and_Methods/Data directory. The processed data used for modeling is available in Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData. All scripts for data processing are located in Materials_and_Methods/Processing_scripts. The Plots_and_Figures directory includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is Main_script.R. <h2>Results</h2> The Results directory contains three subdirectories and the script Models_Outputs.R. The Results/Fitted_models directory includes all .RData files with fitted models for each species. The script Models_Outputs.R generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the Results/Plots folder, while Results/RData contains intermediate .RData files created by Models_Outputs.R to manage computational costs.

Shear Sonic Prediction Using Supervised Machine Learning: Case Study Talang Akar Formation

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

Equipment Design for a Small Binary System Power Plant Using Geothermal Heating Fluid A Case Study

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

Community-Based Waste Management Strategy: Case Study in Wukirsai Tourism Village, Bantul, Yogyakarta

This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.

STFC Astronomy and Artificial Intelligence Case Studies

<h2>Context</h2> As part of the Epistemic Insight Initiative of the LASAR (Learning about Science and Religion) research and outreach centre, an <a href="https://futureofknowledge.com/summer-school/" target="_blank" rel="noopener">STFC Astronomy and Artificial Intelligence summer school with public engagement</a> was held in July 2024. As part of this, an additional free <a href="https://futureofknowledge.com/astronomy-and-ai-online-event/" target="_blank" rel="noopener">Astronomy and AI Online Event</a> day was held the week before on Wednesday 3rd July 2024 to provide panel discussions and example use cases of AI in astronomy, organised by Prof Berry Billingsley and Dr James Pearson. This event hosted two talks by Prof Berry Billingsley and Dr Marc Sarzi, as well as panels of astronomy PhD students and researchers with experience in using AI and deep learning, to discuss the broader questions about AI. The panellists for the event were Ingo Waldmann (University College London), Xinyue Sheng (Queen’s University Belfast), Benjamin Joachimi (University College London), Josh Wilde (The Open University), Weiguang Cui (University of Edinburgh), and Kevin Walsh (Westminster School). The event aimed to reach a wider diversity of students than the summer school: while STFC-funded and self-funded students took priority, any astronomy PhD students could apply, as well as final year undergraduate & master's students in physics and computer science. Alongside this event, we release here a number of recorded case studies showcasing how researchers in astronomy are using AI and deep learning in their own work. These case studies cover a variety of research topics and types of AI, and were kindly provided by a number of researchers and PhD students - more details are given below. As well as their individual topics, many provide tips and explanations for AI, machine learning and deep learning techniques, so we encourage you to watch them all! In addition to the individual case studies, recordings of the online event itself have been made available at the following page, as well as a short hightlight video covering some of the event and case studies: <a href="https://doi.org/10.5281/zenodo.12674686">https://doi.org/10.5281/zenodo.12674686</a> Production Coordinator: Dr James Pearson (The Open University) Video Editor: Dr James Pearson (The Open University) Principal Investigator: Prof Berry Billingsley (Epistemic Insight Initiative) <h2>Case Studies</h2> <h3>Ruby Pearce-Casey (The Open University) - Using cGANs for Anomaly Detection: Hunting for Gravitational Lensing Systems in Euclid</h3> Gravitational lensing is a powerful tool that directly probes all clustering components in the universe through their gravitational effect on light from distant background sources. The problem arises in finding gravitational lenses, and, with the accelerated growth in data volume and complexity in astronomy, machine-learning-aided lens searches have proven successful. We present a proof of concept for an alternative method of strong gravitational lens finding using a conditional Generative Adversarial Network (cGAN). We use Early Release Observation (ERO) images of the Perseus Cluster from Euclid, covering 0.57 sq. degrees on the sky, and the network is based on the pix2pix architecture with an adapted U-Net generator. We train our model to predict Euclid’s NISP-H band flux (1.54-2.00µm) from a combination of the filters NISP-J, NISP-Y and VIS band (0.55-1.54µm) in 40,000 cut-outs from the Perseus Cluster which are 20×20 arcseconds in size. We test the cGAN on 5,000 cut-outs from the Perseus cluster, 10% of which contain a simulated strong gravitational lens painted into the cut-out based on a Singular Isothermal Ellipsoid model. Candidate gravitational lenses and cut-outs with a gravitational lens painted in were deliberated excluded from the model’s training data set such that gravitational lensing systems remain unknown to the network. We find that the cGAN can accurately predict the NISP-H band flux of the cut-outs from the Perseus cluster. However, the model fails to predict the NISP-H band flux of the cut-outs containing the simulated gravitational lenses, with a larger difference between the model’s prediction and ground truth for lenses with extended arcs and Einstein rings, suggesting that the cGAN can be used as an anomaly detector for an alternative method of lens finding. <h3>Xinyue Sheng (Queen’s University Belfast) - NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images</h3> The work is to develop a machine learning classifier (NEEDLE) for identifying superluminous-supernovae and tidal disruption events at their early stage, using a few detections and the context information. It is currently applied on Lasair broker digesting ZTF alerts, and in the future, it will deal with LSST alerts. <h3>Weiguang Cui (University of Edinburgh) - How AI helps with constraining galaxy cluster mass</h3> Machine learning has been widely used in different astronomy researches recently. Some of its models, especially the image to property feature, make it the perfect tool for solving some particular problems in astronomy, such as estimating the cluster mass from observation images. In this showcase, I present how the different ML models are adopted to estimate the cluster mass in different dimensions — from a signal cluster mass M_500 (0d data point, see de Andres et al. 2022), to cluster mass within different radii (1D density profile, Ferragamo et al. 2023) and projected cluster mass map (2D image, de Andres et al. 2024). With this powerful tool, we are able to probe the mystery Universe, deeply, completely and swiftly! <h3>Mike Walmsley (University of Toronto) - Computer vision for galaxy images</h3> Modern telescopes take far more images than humans could ever look through, and so we rely on automated methods to measure what galaxies look like. AI is an excellent tool for solving this visual recognition problem. I’ll talk about how AI models have changed over the last few years to learn efficiently and quickly adapt to new measurement tasks. <h3>Josh Wilde (The Open University) - Using machine learning to find gravitational lenses & how it can go wrong</h3> I will discuss the uses of CNNs to identify gravitational lenses in simulated and real data, discussing common flaws that machine learning models suffer from and what can cause these issues. I will discuss the use of interpretability methods to identify what features within the image the machine learning techniques associate with gravitational lensing. I will explain how the need of interpretability in machine learning has led me to use U-nets to help locate gravitational lenses in images. <h3>Benjamin Joachimi (University College London) - Forward-modelling the Universe with neural density estimation</h3> I provide an overview of recent work constraining cosmological parameters from two of the largest galaxy imaging surveys to date, the Dark Energy Survey and the Kilo-Degree Survey. Both analyses use a novel approach to inference: machine-learning the likelihood using neural density estimators and large suites of large-scale structure simulations. I’ll include brief introductions to weak gravitational lensing, on which the analyses are based, and to the concepts of simulation-based/likelihood-free inference. <h3>Ingo Waldmann (University College London) - Machine learning in exoplanet characterisation</h3> The upcoming decade promises remarkable progress in our grasp of exoplanet formation, evolution, and an in-depth assessment of their climates and possible habitability. With the James Webb Space Telescope now in orbit, we’re at the threshold of high-precision atmospheric studies of these distant worlds. This milestone, combined with forthcoming endeavours like the ELTs and the Ariel space mission, positions us in an era rich in high-precision exoplanetary data. This vast influx of precise data holds immense discovery prospects but also introduces challenges in handling both model intricacy and data volume. As machine and deep learning techniques gain traction across various scientific and industrial domains, they also find their footing in exoplanetary and solar system research. Such techniques offer a refreshing edge in modelling intricate, non-linear data over conventional methods. In my talk’s initial segment, I’ll explore the use of simulation-based inference, deep surrogate models, and Explainable AI in refining time-series data and leveraging inverse modelling to extract exoplanet attributes from observations. In the second part, I’ll delve into our machine-learning-centric data challenges. It’s noteworthy that many challenges in exoplanet data analysis resonate with issues keenly explored in the machine learning realm. Yet, interdisciplinary collaboration has often been stymied by language barriers and domain unfamiliarity. Addressing this, as part of the ESA Ariel Space mission, we have organised <a href="https://www.ariel-datachallenge.space/">four machine learning challenges</a>, hosted at prestigious AI conferences like NeurIPS and ECML, attracting hundreds of participating teams annually. I will discuss how such challenges can catalyse rapid, sturdy AI advancements in this budding domain and pave the way for more resilient AI solutions in the future.

Interview and Observation Result for Post-metaverse Class Implementation "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects""

This data file is open for viewing as proof of Author; Brandon Lie - BINUS University for conffrence paper "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects"  Data can be use an reffrence with author permission*