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1,944 results for “Class I”
An investigation of antihypertensive class, dementia, and cognitive decline
Objective High blood pressure is one of the main modifiable risk factors for dementia. However, there is conflicting evidence regarding the best antihypertensive class for optimising cognition. Our objective was to determine whether any particular class of antihypertensive was associated with a reduced risk of cognitive decline or dementia using comprehensive meta-analysis including reanalysis of original participant data. Methods To identify suitable studies MEDLINE, Embase and PsycINFO® and pre-existing study consortia were searched from inception to December 2017. Authors of prospective longitudinal human studies or trials of antihypertensives were contacted for data-sharing and collaboration. Outcome measures were incident dementia or incident cognitive decline (classified using the reliable change index method). Data were separated into mid and late-life (>65 years) and each antihypertensive class was compared to no treatment and to treatment with other antihypertensives. Meta-analysis was used to synthesize data. Results Over 50,000 participants from 27 studies were included. Among those aged >65 years, with the exception of diuretics, we found no relationship by class with incident cognitive decline or dementia. Diuretic use was suggestive of benefit in some analyses but results were not consistent across follow-up time, comparator group and outcome. Limited data precluded meaningful analyses in those ≤65 years. Conclusions Our findings, drawn from the current evidence base, support clinical freedom in the selection of antihypertensive regimens to achieve blood pressure goals.
Class 97 Network Rail 97302
Class 97 Netword Rail 97302 Modelled in Blender 3.1.2 **** Not Presented as Game Ready **** If you are into your trains, checkout this youtube channel. It's been so helpful to me with my models. https://www.youtube.com/channel/UCRqa3Y2GVMKLyoRmZ80Ep9w Source: Objaverse 1.0 / Sketchfab
Class of 1945 Crest
Class of 1945 Crest from Trident Light at the United States Naval Academy. This model was made with 20 pictures from a Moto G Power processed with 3DF Zephyr Free 5.019. Source: Objaverse 1.0 / Sketchfab
Bioactive compound classes from ChEMBL20 for molecular hierarchy
<p>Compound structure, scaffold, and CSK of 78,150 (all) and 54,042 (only compounds with a scaffold shared by at least one another compound) compounds, respectively, are provided herein. In addition, each compound is annotated with target and potency information.</p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
Activity classes from different categories
<p>A total of 102 activity classes (ACs) were assembled from ChEMBL version 20 and were classified as “easy” (i.e. yielded generally high compound recall using different fingerprints in benchmarking calculations), “preferred/intermediate” (moderate compound recall), and “difficult” (low compound recall). In addition, 10000 randomly selected ZINC compounds were provided. For each compound, both MACCS and ECFP4 fingerprints were given. Furthermore, the general information for these ACs was provided in the excel file.</p> <p> </p> <p> </p>
Tree map plots of women's share across MSC 2010 classes
<p> Hierarchical maps visualising the share of women authors resp. women's publications in zbMATH across the MSC 2010 classes on level 1 and 2. Each rectangle represents a mathematical class and displays the corresponding name and code within the MSC 2010; its size is proportional to the number of authors resp. publications in that field, and the darker the color, the larger the share of women resp. their publications in that field.</p> <p>Detailed information can be obtained using the hover; a right-click allows to get back from MSC level 2 to level 1. Note that the interactive feature requires the activation of JavaScript.</p>
Computational Studies of Human Class V Alcohol Dehydrogenase - The Odd Sibling
<p>A number of class I-VI ADH protein sequences were obtained from the UniProt, NCBI protein database, and Ensembl databases in October 2015. The sequences were aligned using the L-INS-i approach of MAFFT 7.266.</p> <p>The tree was generated using Phyml release 20151210. In the published figure, the tree was visualised as a radial phylogram in Dendroscope 3.</p>
Marx: Uncovering Class Hierarchies in C++ Programs - (Evaluation Data)
<p>Evaluation data of the published paper: "MARX: Uncovering Class Hierarchies in C++ Programs". The paper was published at the Symposium on Network and Distributed System Security (NDSS) 2017.</p> <p>The paper is available at: https://www.syssec.rub.de/research/publications/marx/</p> <p><br> Abstract:</p> <p>Reverse engineering of binary executables is a difficult task which gets more involved by the way compilers translate high-level concepts used in paradigms such as object-oriented programming into native code, as it is the case for C++. Such code is harder to grasp than, e. g., traditional procedural code, since it is generally more verbose and adds complexity through features such as polymorphism or inheritance. Hence, a deep understanding of interactions between instantiated objects, their corresponding classes, and the connection between classes would vastly reduce the time it takes an analyst to understand the application. The growth in complexity in contemporary C++ applications only amplifies the effect.</p> <p>In this paper, we introduce Marx, an analysis framework to reconstruct class hierarchies of C++ programs and resolve virtual callsites. We have evaluated the results on a diverse set of large, real-world applications. Our experimental results show that our approach achieves a high precision (93.2% of the hierarchies reconstructed accurately for Node.js, 88.4% for MySQL Server) while keeping analysis times practical. Furthermore, we show that, despite any imprecision in the analysis, the derived information can be reliably used in classic software security hardening applications without breaking programs. We showcase this property for two applications built on top of the output of our framework: vtable protection and type-safe object reuse. This demonstrates that, in addition to traditional reverse engineering applications, Marx can aid in implementing concrete, valuable tools e. g., in the domain of exploit mitigations.</p>
Empirical Stellar Templates Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>This is the dataset of empirical stellar templates associated with the publication "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra", which has been accepted for publication in the Astrophysical Journal Supplements. </p>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v3.0.2 - November 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>November 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/November-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v3.0.2 - November 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>November 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/November-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v3.0.2 - November 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>November 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/November-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v3.0.2 - November 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>November 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/November-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - September 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>September 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWL</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
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.