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Dataset results
24 results for “Material objects”
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images
<p>We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.</p> <p>Note that there are two files here:</p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a></p> <p>and</p> <p><br> <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set.</p> <p>https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z</p> <p><a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content</p> <p>See ReadMe file in side the downloaded files for more details</p> <p>The full dataset (>100gb) can be found here:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a></p> <p>https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a></p> <p>See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details</p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a></strong></p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a></strong></p> <p> </p> <p><strong>************************************************************************************</strong></p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a></p> <p>The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.</p> <p> </p> <p> </p> <p> </p>
Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Supplementary materials for "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", published in Linguistics: An Interdisciplinary Journal of the Language Sciences
<p>Supplementary files are as follows:</p> <ul> <li><strong>R-code_Scalarity.Rmd</strong> contains the R-code in a markdown version of the statistical analysis of the study.</li> <li><strong>R-code_Scalarity.html</strong> displays the R-code_Scalarity.Rmd in an html format.</li> <li><strong>Results_Scalarity.csv</strong> contains the data of the study.</li> </ul> <p>For more details on the description of the data, see the publication: "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", Linguistics.</p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Support Material for: S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''
<p>The audio corpus used for conducting the listening tests reported in S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''.</p>
Markov Automata with Multiple Objectives: Supplemental Material
<p>This data set contains benchmark files for multi-objective model checking of Markov Automata with <a href="https://www.stormchecker.org/">Storm</a>.</p>
Supplementary material 2 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Flyer: How to collect, store and curate objects in the Anthropocene? On participatory and digital collections.
Supplementary material 1 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Flyer: What is an Anthropocenic Object? Transdisciplinary Perspectives on Natural, Cultural and Hybrid Objects
Supplementary material 4 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Keynote Exhibiting the Anthropocene - and the Technosphere - means breaking away from established knowledge orders
MATERIALS OF ARCHAEOLOGICAL RESEARCH OF THE ARCHAEOLOGICAL HERITAGE OBJECT CULTURAL LAYER OF VORONEZH ON THE TERRITORY OF THE LAND PLOT AT 1, KLUBNAYA STREET AND 30D, 20-LETIYA OKTYABRYA STREET
<p>Archaeological research on the land plot at the address: Voronezh, 1, Klubnaya Street, and 30d, 20-letiya Oktyabrya Street, was carried out as part of the implementation of the section of project documentation to ensure the safety of the identified cultural heritage site "Cultural layer of the city of Voronezh". As a result of the work, additional information was obtained on the history of the coastal part of the city in the XIX-th-XX-th centuries.</p>
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Supplemental Material for "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development"
<p>This package contains the research materials that were used for the paper "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development".</p> <ul> <li><strong>Survey on Goals and Objectives.docx</strong> contains the survey instrument that was used for the study.</li> </ul>
Seurat object input file - Galaxy Training Material
<p>A Seurat input file for conversion to AnnData.</p>
Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Supplemental materials for: Implementation and evaluation of an objective structured clinical examination (OSCE) tool for neurology post-graduate trainees in Lusaka, Zambia
<p>Introduction: Despite the high burden of neurologic disease in Zambia, a low-middle income sub-Saharan African country of 18 million people, there are currently no Zambian neurologists. We established the first neurology residency program in Zambia at the University of Zambia School of Medicine and the University Teaching Hospital in Lusaka. We evaluated the feasibility and effectiveness of a modified Objective Structured Clinical Examination (OSCE) to assess clinical skills. </p> <p>Program Description: The neurology training program began in October 2018, and its 3 participants completed the OSCE exercise in February 2019. The exercise involved using a smartphone to videotape trainees performing a physical examination and oral presentation in the neurology clinic. Trainees and neurology faculty reviewed the videos independently and assessed performance using a standardized rubric. The faculty and trainee then met in person and discussed the activity and scoring rubric. </p> <p>Assessment & Outcomes: Three trainees completed pre- and post-OSCE surveys where they rated their confidence in elements of the history and neurological examination and provided feedback about the OSCE exercise. One faculty member reviewed each video and provided feedback to each trainee. Trainees' average self-confidence scores improved from the pre- to post-OSCE survey in every category (pre-OSCE: mean score 6.84, range 4.8-7.8, SD 0.92; post-OSCE: mean score 7.9, range 5.67-9.33, SD 0.86). Qualitative responses showed trainees found the OSCE helpful, routinely applied feedback, and would appreciate repeating OSCEs every few months. </p> <p>Lessons Learned: This study demonstrated that OSCEs can be modified and successfully implemented in a resource-limited neurology post-graduate training program and result in improved self-confidence among trainees. Important modifications to the OSCE involved using smartphones to videotape the OSCE's and a real patient encounter rather than a standardized patient. Because there are many neurology patients and few providers in Zambia, embedding the experience within a busy clinic day was practical, directly applicable, and at a lower opportunity cost. </p> <p>Future Directions: Future work should expand use of OSCEs both within the Zambian neurology residency program and non-neurology training programs. Including additional video reviewers could add to the validity of clinical skills assessment. Videos created could also be used for remote mentorship and teaching purposes.</p>
Supplementary material 1 from: Sambaraju P (2022) Use of Worksheet events in Excel to save solver objective cell value from each iteration. Research Ideas and Outcomes 8: e79006. https://doi.org/10.3897/rio.8.e79006
BOD data
Supplementary material 5 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Keynote Nature after Europe
Supplementary material 3 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Flyer: Perspectives for the future of conservational institutions and collection practices
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.