Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
38
datasets available to search
ShareScore release 0.9.0
Dataset results
38 results for “Model Repository”
Data Repository: Land surface modelling activities at Weierbach catchment.
<p>The data in this repository comes from the modelling activities with the Community Land Model version 5.0 (CLM5) carried out at the Weierbach catchment, Luxembourg. The repository contains:</p> <ol> <li>A list of matric potentials of <em>Fagus sylvatica </em>at which it experiences a specific loss of conductivity (i.e., 12%, 50%, 88%) obtained from published data [File: additional_PHT_Fagus_sylvatica_Europe.csv].</li> <li>The hourly atmospheric forcing used during the simulations with CLM 5.0 in a NetCDF format [File: atmospheric_forcing.zip].</li> <li>All model results per experiment [model_results.zip].</li> <li>The R scripts for processing the model results for obtaining the information required for each figure [Files: manuscript_figure_#.R].</li> <li>A daily summary of the tree water deficit calculated per PFT, individual tree species, and the whole ecosystem [File: twd.csv].</li> <li>A daily summary of tree transpiration scaled at the catchment level per PFT, individual tree species, and the whole ecosystem [File: et_mm_wei.csv]. This daily summary is based on the hourly data available on: Klaus, J., Fabiani, G., Schoppach, R., Chun, K. P., Iffly, J. F., Penna, D., & Juilleret, J. (2024). Detailed sap flow monitoring data at Weierbach catchment, Luxembourg (Version v01) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11381618" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11381618</a></li> </ol>
European Exposure Model Data Repository
<p>A repository of the exposure data used to develop the ESRM20 exposure models.</p> <p>More information available here: <a href="https://eu-risk.eucentre.it/exposure/">https://eu-risk.eucentre.it/exposure/</a></p>
3D models (NXS): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p><span>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</span></p>
3D models (true color, TIF): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
"Canvas BM" Digital Business Model Template Repository
<p>The Digital Business Model Template Repository consists of 265 unique one-page, diverse business model compositions, constructed as variants or adaptations based on the reference Business Model Canvas (BMC) created by A. Osterwalder. Business Model Canvases are templates composed of key blocks (elements) of the business model, which are interconnected and ready to be filled with content. The process of acquiring templates through quantitative, and then qualitative research, was conducted from November 2020 to October 2022. Each identified template was verified for compliance with licensing rights. The thematic repository should be treated as a business guide, aiding in the selection of suitable tools for designing business models for specific organizations, as well as in the process of creating, analyzing, and modifying business model templates. The Digital Business Model Template Repository can be useful in both a scientific, research, didactic, and individual context, and can also be beneficial in business practice.</p>
Repository of IVD Patient-Specific FE Models
<p>Free repository of 169 PP FE models of the IVD. Resulting cohort from a morphing process as a free-access repository to further empower the scientific community. This initiative underlines our commitment to promoting standardization and facilitating a more comprehensive understanding of the mechanisms underlying IVD degeneration.</p>
Dataset Repository for a Botanical Garden Project: Project Based Learning Assessment from a Blended Approach of PBL with the 5E Model Components
<p><strong>Title:</strong> Botanical Explorers: A Journey Through Our School's Flora - Assessment Data</p><p><strong>Description:</strong> This Excel spreadsheet contains the assessment data for the educational project titled "Botanical Explorers: A Journey Through Our School's Flora", a hands-on science initiative for Grade 9 students at Chalermkwansatree School. The project, conducted under the guidance of Teacher Hasan and aligned with the Additional Science subject focusing on Fuel Energy, is designed to engage students in active learning about local plant life, while developing their research and presentation skills, and fostering environmental appreciation.</p><p>The dataset is part of a comprehensive project contributing 20% to the Term 1, Midterm Score of 50 marks. It encompasses a detailed breakdown of the marks distribution across different tasks such as Data Collection, Book Report, Presentation, and Poster creation, reflecting the multifaceted approach to evaluating student learning and engagement.</p><p><strong>Data Organization:</strong> The spreadsheet is meticulously organized to include:</p><ul><li>A plant list with identifiers like school plant name, location, and space for pictures.</li><li>A marks distribution table indicating the scoring for each project component.</li><li>A timeline for group formation, research, data collection, and submission deadlines.</li><li>Details of the Book Report, Presentation, and Poster requirements.</li></ul><p><strong>Methodology:</strong> Students formed groups to research seven specific plants found within the school premises, examining their identification, classification, ecological roles, growth, and development. The data was collected through a blend of direct observations and scholarly research, ensuring a robust and educational exploration of botany.</p><p><strong>Intended Audience:</strong> The dataset is intended for educational purposes, serving as a valuable resource for educators, students, and researchers interested in project-based learning, botany education, and student assessment methods.</p><p><strong>Usage Notes:</strong> The data provided in this spreadsheet is anonymized, with no personal student information disclosed. It serves as an exemplar model for similar educational initiatives and can be adapted for comparative studies or further educational research.</p><p><strong>Conditions for Use:</strong> The dataset is shared openly with the intention that it will be used for educational and research purposes. Users are requested to cite the dataset appropriately and adhere to any academic and ethical guidelines when utilizing the data for their work.</p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Repository for: "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model"
<p>Set of numerical experiments supporting the paper entitled "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model" by Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard and Marco Toffolon. Submitted to GMD. </p> <p>The folder contains: </p> <p>simulations: DYNO-PODS + Delft3D experiments on Lake Morat. See https://github.com/louisXW/DYNO-pods for more insights on DYNO-PODS and instructions for installation.</p> <p>scripts: extraction and plotting scripts</p> <p>source_code: modified Delft3D src as available at: https://github.com/eawag-surface-waters-research/Delft3D/tree/d3d4/research/surface_heat_transfer</p>
Model data repository of "How sediment thickness influences subduction dynamics and seismicity"
<p>This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness T<sub>sed</sub> of 4 km on a cluster using executables.</p>
The Awareness Assessment Model repository
<p>Dataset of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective". This dataset contains the supplementary materials about:</p> <p>+ the systematic mapping study;</p> <p>+ the taxonomy elaboration;</p> <p>+ the awareness assessment process;</p> <p>+expert panel validation;</p> <p>+ the case study validation;</p> <p>+ R scripts and observations.csv</p> <p>For more information, please get in touch with us (marcio.mantau@gmail.com).</p>
Integrated Model Data Repository
<p>ALLFED integrated food system model supplemental data associated with the paper "Food System Adaptation and Maintaining Trade Greatly Mitigate Global Famine in Abrupt Sunlight Reduction Scenarios"</p>
SCAR DistAnt Ecological Model Output Repository
<p>This repository provides access to a collection of ecological model outputs (species distribution and similar models) from Antarctica and the Southern Ocean. It is a project of the SCAR <a href="https://scar.org/science/egabi/home/" rel="nofollow">Expert Group on Biodiversity Informatics</a> in conjunction with <a href="https://www.belspo.be/belspo/impuls/project_en.stm#ADVANCE" rel="nofollow">ADVANCE</a> (Royal Belgian Institute of Natural Sciences) and the Integrated Digital East Antarctica program at the <a href="https://www.antarctica.gov.au/science/" rel="nofollow">Australian Antarctic Division</a>.</p> <p><strong>Please note:</strong> the inclusion of a layer in this collection is not an endorsement of its quality or suitability for your intended purpose. Users should consult the associated publication for details on the source data and modelling processes. We encourage users to contact the original publication authors to discuss their intended use of these layers.</p> <p>Model outputs are provided as cloud-optimized geotiffs (COGs), with generally one file per species. The COG will have multiple bands if the original model predictions include uncertainty estimates or multiple model output variables. Each output has been kept on its original coordinate reference system (map projection) and spatial resolution.</p> <p>There is a <a href="https://github.com/SCAR/distant/blob/master/metadata.csv">table of minimal metadata</a> that describes the layers in the collection. This metadata is intended to be sufficient for users to find potential layers of interest, and make an initial evaluation of their high-level characteristics such as taxonomic details, spatial coverage and resolution, and model output types. Our metadata is NOT intended to provide a comprehensive description of each layer: users are referred to the original publication for that level of detail (see the layer’s <code>reference</code> entry).</p> <div> <h3>Citing</h3> The layers in this collection have been re-released under their original license where applicable, or a CC-BY licence otherwise. Please cite when using, and also cite the original data sources used. For example:</div> <div> <p>Kovacs J (2020) A model of my favourite Southern Ocean species. <em>Journal of Southern Ocean Stuff</em> <strong>123</strong>:1–10. Data obtained from the SCAR DistAnt Ecological Model Output Repository, doi:10.5281/zenodo.10910076</p> </div> <p>Individual data sources might have varying licence conditions: consult the <code>licence</code> field for details.</p>
Data repository for study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate"
<p>Data for the study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate".</p> <p><strong>hindcasting_analysis</strong></p> <ul> <li>figures of the hindcasting exercise in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>baseline scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure and export restriction scenario -- <em>agrimate_baseline=2007-2009_export_restrictions=2007-2011_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> </ul> </li> </ul> </li> </ul> <p><strong>multibreadbasket_analysis</strong></p> <ul> <li>figures of the multibreadbasket analysis in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>simulations under historical climatic conditions with <number> as an identifier -- <em>agrimate_his-<number>.nc</em></li> <li>simulations under +2°C projection with <number> as an identifier -- <em>agrimate_2p0-<number>.nc</em></li> </ul> </li> </ul> </li> <li>processed_data <ul> <li>processed output data to easier/faster plot</li> </ul> </li> </ul> <p><strong>sensitivity_analysis</strong></p> <ul> <li>raw data and graphics as in <strong>main_output</strong> for different model parameters as given in Table F.1</li> </ul> <p> </p> <p> </p>
Model and Data Repository_CLEWs 4 Zambia
<p><span>This repository for the research affiliated with the University of Edinburgh encompasses a comprehensive collection of data and model files, meticulously curated to facilitate detailed analysis and modelling using the CLEWs (Climate-Land-Energy-Water) framework. The repository includes the following essential files:</span></p> <ol> <li><strong><span>CLEWs Scenario Data Files</span></strong></li> <li><strong><span>CLEWs Reference Diagram for the Baseline</span></strong><span>:</span></li> <ul> <li><span>A comprehensive PDF diagram illustrating the baseline interconnections within the CLEWs framework for Zambia. This diagram serves as a visual aid to understand the foundational relationships and interactions between climate, land, energy, and water systems.</span></li> </ul> <li><strong><span>CLEWs Number Crunching File</span></strong><span>:</span></li> <ul> <li><span>An Excel file containing detailed numerical analyses and computations essential for the CLEWs modelling. This file includes various datasets, calculations, and results that form the backbone of the scenario analyses.</span></li> </ul> <li><strong><span>Signed Stakeholder Consent Forms</span></strong><span>:</span></li> <ul> <li><span>Documentation of consent from stakeholders who contributed to the research, ensuring ethical standards and transparency in data collection and usage.</span></li> </ul> <li><strong><span>Detailed Stakeholder Responses</span></strong><span>:</span></li> <ul> <li><span>Comprehensive documentation of feedback and insights from stakeholders, providing valuable qualitative data that complement the quantitative analyses. These responses are crucial for understanding local perspectives and validating model assumptions.</span></li> </ul> <li><strong><span>GEOCLEWs Inputs and Outputs for Zambia</span></strong><span>:</span></li> <ul> <li><span>A collection of files generated by the GeoCLEWs_ZM script, which automates data collection from sources such as GAEZ v4 and FAOSTAT. The outputs include agro-climatic potential yield, crop water deficit, precipitation, and land cover data, combined with electricity information for detailed CLEWs modelling. This dataset is vital for integrated analysis and visualization of the CLEWs components.</span></li> </ul> </ol> <p><span>By centralising these critical resources, the Zenodo repository provides an invaluable tool for researchers, policymakers, and stakeholders engaged in sustainable development and climate resilience efforts in Zambia. Each file has been meticulously prepared and uploaded to ensure ease of access, facilitating robust analysis and informed decision-making within the context of the CLEWs framework.</span></p>
Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"
<p>Dataset to accompany the publication <em>“A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi”</em> by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.</p> <p>The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at<a href="https://github.com/UCL/TLOmodel"> https://github.com/UCL/TLOmodel</a>. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".</p> <p>This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:</p> <p>"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.</p> <p>"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.</p> <p>The data files included in this repository are:</p> <p><strong>DALYS_by_cause_with_time.csv</strong>: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.</p> <p><strong>HSIs_requested_by_type_and_facility_level_with_time.csv</strong>: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.</p> <p><strong>HSIs_delivered_by_type_and_facility_level_with_time.csv</strong>:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.</p> <p><strong>Population_with_time.csv</strong>:total population size on a given year. </p> <p> </p> <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.