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.
12
datasets available to search
ShareScore release 0.7.1
Dataset results
12 results for “Model Template”
"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>
Data generated for the publication: Keeping it in the family: Using protein family templates to rescue poor AlphaFold models unliked
<p>Data and manuscript of:</p> <p>Keeping it in the family: Using protein family templates to rescue low confidence AlphaFold2 models</p> <p>Francesco Costa1, Matthias Blum1 and Alex Bateman1</p> <ol> <li>European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton. CB10 1SD. UK</li> </ol> <ul> <li>results contains the workflow results;</li> <li>AF2_seed contains results of the comparison with AF2 run with multiple seeds;</li> </ul>
Dataset for: Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates
<p>Published in:<br> Nature Biomedical Engineering. doi: 10.1038/s41551-020-0566-1.</p> <p>Generation of model tissues with dendritic vascular networks via sacrificial laser-sintered carbohydrate templates</p> <p>Ian S. Kinstlinger (1), Sarah H. Saxton (2), Gisele A. Calderon (1), Karen Vasquez Ruiz (1), David R. Yalacki (1), Palvasha R. Deme (1), Jessica E. Rosenkrantz (3), Jesse D. Louis-Rosenberg (3), Fredrik Johansson (2), Kevin D. Janson (1), Daniel W. Sazer (1), Saarang S. Panchavati (1), Karl-Dimiter Bissig (4), Kelly R. Stevens (2,5), and Jordan S. Miller (1)</p> <p>1 Department of Bioengineering, Rice University, Houston, TX, USA.<br> 2 Department of Bioengineering, University of Washington, Seattle, WA, USA.<br> 3 Nervous System, Palenville, NY, USA.<br> 4 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.<br> 5 Department of Pathology, University of Washington, Seattle, WA, USA</p> <p>Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D-printing techniques. Here we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging as a result of fluid convection through these networks and of diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function.</p>
Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
<p>Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D<sub>2</sub> dopamine (D<sub>2</sub>R) and serotonin 5-HT<sub>2A</sub> receptors (5-HT<sub>2A</sub>R) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D<sub>2</sub>R and 5-HT<sub>2A</sub>R crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs displayed ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even displayed ligand enrichment better than that of the D<sub>2</sub>R and 5-HT<sub>2A</sub>R crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.</p>
Focused learning by antibody language models using preferential masking of non-templated regions
<p><strong>Motivation.</strong> While existing antibody language models (AbLMs) excel at predicting germline residues, they often struggle with mutated and non-templated residues, which concentrate in the complementarity-determining regions (CDRs) and are crucial for determining antigen-binding specificity. Many of these models are trained using a masked language modeling (MLM) objective with uniform masking probabilities; however, antibody recombination is modular in nature, creating relatively distinct regions of high and low complexity (non-templated and templated, respectively). We sought to determine whether and to what extent AbLMs can improve when trained using an alternative masking strategy based on this observation.</p> <p><strong>Results.</strong> We developed a variation on MLM called <strong><em>Preferential Masking</em></strong>, which alters masking probabilities to amplify training signals from the CDR3. We pre-trained two AbLMs using either uniform or preferential masking and observed that the latter improves pre-training efficiency and residue prediction accuracy in the highly variable CDR3. Preferential masking also improves antibody classification by native chain pairing and binding specificity, suggesting improved CDR3 understanding and indicating that non-random, learnable patterns help govern antibody chain pairing. We further show that specificity classification is largely informed by residues in the CDRs, demonstrating that AbLMs learn meaningful patterns that align with immunological understanding.</p> <p><strong>Files. </strong>The following files are included in this repository:</p> <ul> <li><strong><em>uniform_250k.tar.gz</em></strong>: Model weights for the Uniform-250k model.</li> <li><strong><em>uniform_350k.tar.gz</em></strong>: Model weights for the Uniform-350k model.</li> <li><strong><em>preferential_250k.tar.gz</em></strong>: Model weights for the Preferential-250k model.</li> <li><strong><em>train-eval-test_cdr-mask.tar.gz</em></strong>: Datasets used to train all three models above. Compressed folder containing three files: <em>A_train.csv</em>, <em>A_eval.csv</em>, and <em>B_test.csv</em>. Each row contains a natively paired sequence with its corresponding label-encoded CDR mask, designed to align with the tokenized amino acid sequence. Sequences were obtained from <a href="https://doi.org/10.1038/s41586-022-05371-z">Jaffe et al.</a> and <a href="https://doi.org/10.1016/j.celrep.2024.114307">Hurtado et al</a>. These are referenced in the paper as Dataset A (<em>A_train.csv, A_eval.csv)</em>, and Dataset B (<em>B_test.csv</em>)<em>.</em></li> <li><strong><em>test-set_annotations.tar.gz</em></strong>: Unpaired annotations for all test set (Dataset B) sequences: <em>B_test-set_annotations.csv</em>. Used for Fig. 3 and Fig. 4D. Annotations can be mapped back to the paired sequences using their `sequence_id` and `locus` information.</li> <li><strong><em>pair_classification.tar.gz</em></strong>: Two classification datasets used to train the classifier models in Figure 4: <em>C_native-0_shuffled-1.csv</em> (Dataset C) and <em>D_native-0_shuffled-1.csv</em> (Dataset D). Dataset C sequences were obtained from <a href="https://doi.org/10.1038/s41586-022-05371-z">Jaffe et al.</a> and <a href="https://doi.org/10.1016/j.celrep.2024.114307">Hurtado et al</a> (Dataset B), and Dataset D sequences were obtained from <a href="https://doi.org/10.1038/s41590-022-01230-1">Phad et al</a> and data generated as part of this study.</li> <li><strong><em>CoV_classification.tar.gz</em></strong>: Classification dataset used to train the classifier models in Figure 5: <em>E_hd-0_cov-1.csv </em>(Dataset E). CoV antibody sequences were obtained from <a href="https://doi.org/10.1093/bioinformatics/btaa739">CoV-AbDAb</a>, and healthy donor sequences were obtained from <a href="https://doi.org/10.1038/s41590-022-01230-1">Phad et al</a>.</li> </ul> <p><strong>Code.</strong> All code used for model training, testing, and figure generation is available under the MIT license on <a href="https://github.com/brineylab/preferential-masking-paper">GitHub.</a></p> <p> </p>
Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)
<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>
Graph models underlying templates for annotating a study in ecology and evolution
<p><em>Schematic representation of the graph models, resources and links to ontologies underlying a set of templates designed to annotate studies in invasion biology, and more generally ecology or evolution. The main template is the “General scoping” template, while each other box would allow a more detailed description of Dataset, Study system, Study design, and research question and hypotheses in Invasion biology. This figure was presented during the <a href="https://hiknowledgeworkshops.com/workshop-2/">June 2023 workshop of the Hi Knowledge initiative</a>. </em></p>
CRASHS templates and models
Open the record for dataset details and reuse information.
Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity
Open the record for dataset details and reuse information.
A Model-driven and Template-based Approach for Requirements Specification : Case studies
<p>This dataset includes the requirements specified using our templates as part of our case studies :</p> <ol> <li>ARINC-653 case study : Time management requirements.</li> <li>ARINC-653 case study : Intra-partition communication requirements.</li> <li>AUTOSAR case study : Crypto service manager requirements.</li> <li>Traditional software case study : Requirements from Kaggle.</li> </ol>
Data from: Evaluating the reliability of microsatellite genotyping from low-quality DNA templates with a polynomial distribution model
Open the record for dataset details and reuse information.
EUGAIN Template Role Model Celebration
Open the record for dataset details and reuse information.
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.