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4,594 results for “Design”
Valleywide Bird Survey, Hubbard Brook Experimental Forest, 1999-2016 (ongoing) (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-hbr/178/3. The abstract below was extracted from the Level 0 data package and is included for context: This data set includes bird point-count data collected during the breeding seasons (May-July) of 1999-2016 at 373 locations throughout Hubbard Brook Experimental Forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data from Bieri Thesis: Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs - 2022
This dataset consists of spreadsheets used in the creation of: "Elizabeth Bieri, Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs. MS Thesis, University of Virginia, Charlottesville, VA. Advisors: Matthew Reidenbach & Patricia Wiberg, 2022" (https://doi.org/10.18130/2b04-wz72). Documentation on methods and types of data are given in the thesis and are not repeated here. There are two .zip files. One contains the original Excel workbooks. The other contains the data from individual sheets within the workbooks as comma-separated-value (.csv) text files. The file listing for the CSV files are: Archive: Bieri_Comma_separated_Value_Files.zip Length Date Time Name --------- ---------- ----- ---- 0 2025-12-15 12:47 Bieri_Comma_separated_Value_Files/ 1787 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_crests.csv 239 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S4.csv 273 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S7.csv 652 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed.csv 476 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed_no_reef.csv 102 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S4.csv 97 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S7.csv 297 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_july.csv 290 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_oct.csv 399 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_All.csv 509 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_July2021.csv 507 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Oct2021.csv 1242 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sed_OM.csv 516 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sept2020.csv 913981 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS4.csv 715881 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS7.csv 5251
Molecular datasets from "SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design"
<p>Herein find the molecular datasets from "<a href="https://chemrxiv.org/articles/SMILES-Based_Deep_Generative_Scaffold_Decorator_for_De-Novo_Drug_Design/11638383">SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design</a>". These were generated with SMILES-based scaffold decorator generative models trained with two training sets (DRD2 and ChEMBL). These generative models require a partially-built molecule (scaffold) as input and output several possible completions for each scaffold. Each dataset corresponds to a model trained with the ChEMBL or DRD2 sets, wither multi-step (ms) or single-step (ss) and the provenance of the scaffolds (validation set, or non-dataset).</p> <p>The molecules generated are annotated with a set of descriptors. The DRD2 datasets have the predicted probability of each molecule to be active on DRD2 (p) obtained from a Random Forest model. The ChEMBL model's descriptors are related to the synthesizability of the molecules (see manuscript). Also, the datasets decorated from validation set scaffolds are annotated whether they are part of the validation set (in_validation).</p>
Design of Multifunctional Composites: New Strategy to Save Energy and Improve Mechanical Performance
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
Diffraction images used to solve the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-alpha-1,2-Mannanases."
<p>Raw diffraction images used for generating the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-a-1,2-Mannanases" (available <a href="https://doi.org/10.1002/chem.201800435">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Co-design – Part 1: Workshops to explore current imaginaries behind smart home technologies development and use
<h3>Description</h3> <p>This qualitative dataset is the <strong>first part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of independent <strong>in-person workshops</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The data collected during the subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals</h3> <ul> <li><strong>P1_WSP-PRO-TRANSCR_R02.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-PRO-VIS_000 </strong>till _<strong>013.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with early-adopters</h3> <ul> <li><strong>P1_WSP-EA-TRANSCR_R01.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-EA-VIS_000 </strong>till _<strong>011.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P1_WSP-LN-TRANSCR_R00.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-LN-VIS_000 </strong>till _<strong>012.jpg</strong> (participant-generated visual data)</li> </ul> <h3> </h3> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Co-design – Part 2: Workshop with professionals, early-adopters, and late/non-adopters to design interventions for a more responsible and just future with smart home technologies
<h3>Description</h3> <p>This qualitative dataset is the <strong>second part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of two <strong>in-person workshops</strong>: one with professionals developing smart technology and its early-adopters, and a second one with late/non-adopters of smart technology. The first workshop had four groups of participants and the second three groups. The data is divided by each group. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals and early-adopters</h3> <ul> <li><strong>P2_WSP-PA-G1-TRANSCR_R00.docx</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-PA-G1-VIS_000 </strong>to <strong>_005</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G2-TRANSCR_R00.docx </strong>(transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-PA-G2-VIS_000 </strong>to<strong> _008</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G3-TRANSCR_R00.docx </strong>(transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-PA-G3-VIS_000 </strong>to<strong> _004</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G4-TRANSCR_R00.docx </strong>(transcription of group 4 audio recordings) <ul> <li><strong>P2_WSP-PA-G4-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P2_WSP-LN-G1-TRANSCR_R00</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-LN-G1-VIS_000 </strong>and<strong> _001</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G2-TRANSCR_R00</strong> (transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-LN-G2-VIS_000 </strong>to<strong> _003</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G3-TRANSCR_R00</strong> (transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-LN-G3-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Code and Data for "Anticoncentration and state design of random tensor networks"
<p>We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ∼γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.</p>
A consensus compound/bioactivity dataset for data-driven drug design and chemogenomics
<p>This is the <strong>updated version</strong> of the dataset from <strong>10.5281/zenodo.6320761</strong></p> <p><strong>Information</strong></p> <p>The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising <a href="tel:1144648">1144648</a> compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design.</p> <p>The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation.</p> <p>This dataset belongs to the publication: <a href="https://doi.org/10.3390/molecules27082513">https://doi.org/10.3390/molecules27082513</a><br> </p> <p><strong>Structure and content of the dataset</strong></p> <table align="left"> <caption><strong>Dataset structure</strong></caption> <thead> <tr> <th scope="col"> <p>ChEMBL</p> <p>ID</p> </th> <th scope="col"> <p>PubChem</p> <p>ID</p> </th> <th scope="col"> <p>IUPHAR</p> <p>ID</p> </th> <th scope="col">Target</th> <th scope="col"> <p>Activity</p> <p>type</p> </th> <th scope="col">Assay type</th> <th scope="col">Unit</th> <th scope="col">Mean C (0)</th> <th scope="col">...</th> <th scope="col">Mean PC (0)</th> <th scope="col">...</th> <th scope="col">Mean B (0)</th> <th scope="col">...</th> <th scope="col">Mean I (0)</th> <th scope="col">...</th> <th scope="col">Mean PD (0)</th> <th scope="col">...</th> <th scope="col">Activity check annotation</th> <th scope="col">Ligand names</th> <th scope="col">Canonical SMILES C</th> <th scope="col">...</th> <th scope="col">Structure check (Tanimoto)</th> <th scope="col">Source</th> </tr> </thead> <tbody> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p>The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file.</p> <p>Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the <strong>File Reader</strong> node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format.</p> <p>Column content:</p> <ul> <li>ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases</li> <li>Target: biological target of the molecule expressed as the HGNC gene symbol</li> <li>Activity type: for example, pIC<sub>50</sub></li> <li>Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified</li> <li>Unit: unit of bioactivity measurement</li> <li>Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database</li> <li>Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence <ul> <li>no comment: bioactivity values are within one log unit;</li> <li>check activity data: bioactivity values are not within one log unit;</li> <li>only one data point: only one value was available, no comparison and no range calculated;</li> <li>no activity value: no precise numeric activity value was available;</li> <li>no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration</li> </ul> </li> <li>Ligand names: all unique names contained in the five source databases are listed</li> <li>Canonical SMILES columns: Molecular structure of the compound from each database</li> <li>Structure check (Tanimoto): To denote matching or differing compound structures in different source databases <ul> <li>match: molecule structures are the same between different sources;</li> <li>no match: the structures differ. We calculated the Jaccard-Tanimoto similarity coefficient from Morgan Fingerprints to reveal true differences between sources and reported the minimum value;</li> <li>1 structure: no structure comparison is possible, because there was only one structure available;</li> <li>no structure: no structure comparison is possible, because there was no structure available.</li> </ul> </li> <li>Source: From which databases the data come from</li> </ul> <p> </p> <p> </p>
Dataset for "Large Language Models as molecular design engines"
<ol> <li><strong>claude-gpt-paper.zip :</strong><br><br>This dataset contains data and results associated with the paper "Large Language Models as molecular design<br>engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).</li> </ol> <p>The dataset includes:</p> <ol> <li>PM7 MOPAC energy calculations for generated molecules, along with their SMILES representations and molecule IDs.</li> <li>PM7-calculated charges for the generated molecules.</li> <li>Output files from the Claude 3 Opus language model for each prompt category along.</li> <li>Original dataset (subset of ZINC database) used to build common keys and the initial design space.</li> <li>JSON file containing common keys for featurizing unknown SMILES.</li> <li>PCA object to convert molecule embeddings to 3-dimensional embeddings.</li> </ol> <p>The data is organized into the following folders:</p> <ul> <li><code>pm7_charge_results</code>: Contains HOMO-LUMO energy differences for plotting.</li> <li><code>pm7_charge_calculation</code>: Contains PM7 MOPAC energy calculations and charges.</li> <li><code>out</code>: Contains output files from the Claude 3 Opus language model.</li> <li><code>fact-dropbox</code>: Contains the original dataset, common keys, and PCA object file.</li> </ul> <p>The data can be used to reproduce the results presented in the paper and serve as a foundation for further research in this area.</p> <p>For a detailed description of the folder structure and contents, please refer to the File_descriptions.md file included in the dataset.<br><br><br>2. llm-visulizer-dashapp.zip<br><br>This is the code for the visualizer app for viewing the molecules generated by the LLM. The README.md file has details about running the app.</p> <p>3. claude-gpt-paper-codes.zip </p> <p>This contains the notebook GPT_modification_just_plots.ipynb for plotting, and other codes. The README.md file has details about running the main notebook for getting the plots.</p>
Dataset for the paper "Designs in finite classical polar spaces"
<div> <div><span>This repository contains the designs from the paper </span><span>"Designs in finite classical polar spaces" by Michael Kiermaier, Kai-Uwe Schmidt, and Alfred Wassermann, </span><span>in Designs, Codes, and Cryptography. </span></div> <div> </div> <div> <div> <div><span>All designs in this repository are simple designs.</span></div> <br> <div><span>The file format is JSON. Each file contains the designs in a fixed finite polar space </span><span>for a pair of parameters t and k. </span></div> <div> </div> <div><span>The file README.md contains more detailed information about the file format.</span></div> </div> </div> </div>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Machine learning designs non-hemolytic antimicrobial peptides
<p>The upload contains additional primary data associated with the publication, including raw data in the original file format whenever possible.</p> <p>Data content: HRMS, HPLC-MS, CD, MD, TEM</p>
Dataset and replication information for It's About Time: How to Study Intertemporal Choice in Systems Design
<p>Dataset and replication package for the paper <em>It's About Time: How to Study Intertemporal Choice in Systems Design</em> (Fagerholm, F., De los Ríos, A., Cárdenas Castro, C., Gil, J., Chatzigeorgiou, A., Ampatzoglou, A., Becker, C. (2023). It’s About Time: How To Study Intertemporal Choice in Systems Design. Information and Software Technology.). The dataset consists of answers to a scenario-based questionnaire that collects data on intertemporal choice in the context of software development. An analysis script is provided to show the details of the calculations and analyses performed for the paper. The replication package includes the protocol for data collection sessions and different versions of the task scenario and questionnaire. More information is given in the description file.</p>
Integrating Co-design activities in agile development
<p>Within the Cos4Cloud project the agile methodologies recommended the instrument of co-design digests. This instrument was intended to form a bridge between co-design and the actual agile development process. During the project, the final process was elaborated closely with the co-design team, and was documented in the project's wiki page.</p> <p>The process includes the following steps:</p> <ul> <li>Set up a co-design digest page in the wiki</li> <li>co-design session is taking place</li> <li>results are collected in a co-design report (as it used to be)</li> <li>essential feedback/requirement will be added to the digest page</li> <li>Note to the developer team to review and update the digest table</li> <li>Developer team refines and decides on each entry in the digest table and links to user</li> <li>stories in their backlog (does not have to be accessible, though)</li> <li>Developer team updates the digest table during development of related user stories</li> <li>(decisions, implementation status, changes to the story, ...)</li> <li>Occasionally development team is reminded to update the digests</li> </ul> <p>Partners were invited to adopt this intermediary step to densify, and evaluate the outcome of co-design sessions before further integrate them into the actual agile development process.</p> <p>For one co-design session 52°North and Science for Change prepared a session where participants were asked to think in user stories. The outcome was a list of user stories which only had to be reviewed and refined. For such sessions the development teams chose to make evaluation and integration into the backlog an ad-hoc task and skipped the extra creation of a digest to avoid duplicated work. A proximity of co-design output and agile input naturally accelerates the intermediary step of a Co-design digest and its integration work or (in the extreme) even makes it superfluous.</p>
Modulation Engineering: Stimulation Design for Enhanced Kinetic Information from Modulation-Excitation Experiments on Catalytic Systems
<p>Dataset used in the publication "Modulation Engineering: Stimulation Design for Enhanced Kinetic Information from Modulation-Excitation Experiments on Catalytic Systems" (<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Facscatal.3c00646&data=05%7C01%7CValentijn.DeCoster%40UGent.be%7C2a7a2c81f646405654d308db2f58f8ec%7Cd7811cdeecef496c8f91a1786241b99c%7C1%7C0%7C638155831400735344%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=e%2Fyp50KsegsMtKcY9ijwh3AqUbrsxFLo%2BTXyGmzOLps%3D&reserved=0">https://doi.org/10.1021/acscatal.3c00646</a>).<br> A description document ("Data overview.docx") is included and provides an overview of the dataset.</p>
nanoindentation data associated with the publication "On the elastic microstructure of bulk metallic glasses" in Materials&Design 2023
<p>This dataset consists of indentation data measured with a conospherical tip in a Hysitron-Bruker TI980 Nanoindenter on the surface of a <100> Silicon wafer and a polished cross-sectional cut of a Zr65Cu25Al10 bulk metallic glass.</p> <p>It is associated with the following publication: <br>Birte Riechers, Catherine Ott, Saurabh Mohan Das, Christian H. Liebscher, Konrad Samwer, Peter M. Derlet and Robert Maass "On the elastic microstructure of bulk metallic glasses" Materials and Design 229, (2023) 111929. https://doi.org/10.1016/j.matdes.2023.111929</p> <p>All experimental information can be found in this paper and in the accompanying supplementary information.</p> <p>This electronic version of the data was published on the "Zenodo Data repository" found at http://zenodo.org/deposit in the community "Bundesanstalt fuer Materialforschung und -pruefung (BAM)".</p> <p>The authors have copyright to these data. You are welcome to use the data for further analysis, but are requested to cite the original publication whenever use is made of the data in publications, presentations, etc. </p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data format is defined as described below:</p> <p>In total, Fifteen text files exist that result in five different data sets.</p> <p>Two data sets represent measurements on Silicon, these are specifically the topography (mapped height profile) and indentation modulus (Si-topography.txt and Si-modulus.txt). The connected lateral information of these mapped quantities (i.e. Si-X_topography.txt and Si-Y_topography.txt; Si-X_modulus.txt and Si-Y_modulus.txt). This amounts to six .txt files connected to measurements on Silicon.</p> <p>Three data sets represent measurements on the Zr65Cu25Al10 bulk metallic glass. These are specifically the topography (MG-topography.txt), the indentation modulus (MG-modulus.txt), and the curvature-corrected indentation modulus (MG-curvcorr_modulus.txt). The connected lateral information of these mapped quantities (i.e. MG-X_topography.txt and MG-Y_topography.txt; MG-X_modulus.txt and MG-Y_modulus.txt; MG-X_curvcorr-modulus.txt and MG-Y_curvcorr-modulus.txt). This amounts to nine .txt files connected to measurements on the metallic glass.</p> <p>The files are plain text files with the data points separated by commata. Topography data is stated in units of Nanometer, modulus data is stated relative to its mean as unit-less values.</p> <p>Beside the .txt files, one figure (.pdf) with a plot of each data set is provided for reference, and the python code (Riechers_OnTheElasticMicrostructureOfBulkMetallicGlasses_zenodo.ipynb) generating these figures from the data sets is uploaded to this repository as well.</p>
Hybrid quantum-classical machine learning for generative chemistry and drug design: Generated molecules
<p>Deep generative chemistry models emerge as powerful tools to expedite drug discovery. How- ever, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The pre- sented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.</p>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface
<p>Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.</p>
Deliverable 2.3 Design Load Case Database for Code-to-Code Comparison
<p>In work package 2 of FLOATECH a detailed validation and verification of the capabilities of QBlade-Ocean was performed. Thereby, three wind turbine models mounted on floating substructures with differing characteristics serve as the means for the validation.This dataset contains Floating Offshore Wind Turbine (FOWT) calculations in various design situations, computed with three different codes. In more detail, three floating platform archetypes are used in the code-to-code comparison ongoing in work package 2: a semi-submersible-type floater and a spar-type floater as well as the Hexafloat® concept recently proposed by Saipem®. The three test-cases are the NREL 5MW RWT mounted on the DeepCwind semi-submersible platform, the DTU 10MW RWT mounted on the SOFTWIND spar-type platform and the DTU 10MW RWT mounted on the Hexafloat® platform. <br> More details regarding the dataset structure and the testcases can be found in the accompanying document. </p> <p> </p> <p><em>Changelog:</em> </p> <p>Version 5.0.0</p> <ul> <li>Changed structural damping ratio (increased) in OpenFAST results (SOFTWIND and Hexafloat models) to match structural damping ratios in QBlade</li> <li>Added wave time series at platform undisplaced position</li> <li>Added TurbSim input files for wind field generation. TurbSim v2.0.0 (https://www.nrel.gov/wind/nwtc/turbsim.html - National Renewable Energy Laboratory)</li> <li>Added detailed DLC and simulation information in "DLC&SimulationList.xlsx"</li> </ul> <p>Version 4.0.0</p> <ul> <li>Correction of bugs in QB 5MWOC4 and 10MWSOFT models: wind shear exponent in wind fields (0.11 -> 0.14)</li> <li>Update of hydrodynamic database used in OpenFAST and QBlade results of 10MWSOFT model </li> </ul> <p> </p> <pre><code>Data in version 5.0.0 is used in WES publication: Papi, F., Troise, G., Behrens de Luna, R., Saverin, J., Perez-Becker, S., Marten, D., Ducasse, M.-L., and Bianchini, A.: A Code-to-Code Comparison for Floating Offshore Wind Turbine Simulation in Realistic Environmental Conditions: Quantifying the Impact of Modeling Fidelity on Different Substructure Concepts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-107, in review, 2023.</code></pre> <p> </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.