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1,549 results for “benchmarks”
Benchmark FIB SEM and Airyscan data of HeLa cells
<p>Sample volume electron microscopy dataset cropped from EMPIAR-10819 and fluorescence dataset from BioImage Archive S-BSST707 for the CLEM-Reg paper.</p>
Benchmark Data for Attracting Cavities 2.0 Small-Molecular Docking Program
<p>This repository provides data from the following article:<br> <br> U.F. Roehrig, M. Goullieux, M. Bugnon, V. Zoete,<br> Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking.<br> J. Chem. Inf. Modeling 2023<br> https://doi.org/10.1021/acs.jcim.3c00054<br> <br> </p>
PheKnowLator Human Disease Knowledge Graph Benchmarks -- v1.0.0
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v1.0.0)</strong></p><p><strong>Build Date: September 03, 2019</strong></p><p>The KG Benchmark Builds can also be downloaded from Zenodo:<br>👉 <strong>KGs:</strong> <a href="https://doi.org/10.5281/zenodo.7030200">https://doi.org/10.5281/zenodo.7030200</a><br>👉 <strong>Embeddings:</strong> <a href="https://zenodo.org/record/7030189">https://zenodo.org/record/7030189</a></p><p> </p><p><strong>Required Input Documents</strong></p><ul><li>resource_info.txt</li><li>class_source_list.txt</li><li>instance_source_list.txt</li><li>ontology_source_list.txt</li></ul><p> </p><p><strong>Data</strong></p><p><strong>Data Download Date:</strong> November 30, 2018</p><p><i><strong>Ontologies</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology</a></li><li><a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology</a></li></ul><p><i><strong>Classes</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/doid.owl">Human Disease Ontology</a></li><li><a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">Gene Ontology: gene associations</a></li><li><a href="https://reactome.org/download/current/gene_association.reactome">Reactome: gene associations</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">Human Phenotype Ontology: all source annotations - genes to phenotypes</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastSt">Human Phenotype Ontology: all source annotations - diseases to genes to phenotypes</a></li></ul><p><i><strong>Instances</strong></i></p><ul><li><a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">CTD: chemicals-genes</a></li><li><a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">CTD: chemicals-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">CTD: chemicals-diseases</a></li><li><a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">CTD: genes-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">CTD: diseases-pathways</a></li><li><a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">STRING DB: Proteins</a></li><li><a href="https://string-db.org/mapping_files/entrez_mappings/entrez_gene_id.vs.string.v10.28042015.tsv">String DB: entrez gene mappings</a></li></ul><p> </p><p><strong>Knowledge Graphs</strong></p><p><strong>Knowledge Representation</strong><br>We worked with a PhD-level biologist to develop a knowledge representation (see the figure below) that modeled mechanisms underlying human disease.</p><p> </p><p>To do this, we manually mapped all possible combinations of the following six node types:</p><ul><li>Humans Diseases</li><li>Human Phenotypes</li><li>Human Genes</li><li>Gene Ontology concepts</li><li>Reactome Pathways</li><li>Chemicals</li></ul><p>As shown in the figure above, the <a href="http://basic-formal-ontology.org/">Basic Formal Ontology</a> and <a href="https://github.com/oborel/obo-relations/">Relation Ontology</a> ontologies were then used to create edges between the node types.</p><p> </p><p>As shown in this figure, the following edge-types were created:</p><ul><li><strong>Phenotypes-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology (HP)</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">phenotype-Entrez gene annotations</a> that were used to map 6,651 HP classes to 120,288 Entrez genes.</li><li><strong>Phenotypes-Diseases:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">HP</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_diseases_to_genes_to_phenotypes.txt">HP-DOID-Gene annotations</a> that were used to map 5,438 HP concepts to 43,817 DOID concepts.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology (GO)</a> provides <a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">GO-Gene annotations</a> that were used to map 17,505 GO concepts to 265,002 Entrez genes.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Pathways-Pathways:</strong> <a href="https://reactome.org/">Reactome</a> provides <a href="https://reactome.org/download/current/gene_association.reactome">GO-Gene links</a> that were used to map 17,906 pathways to 1,910 biological processes, molecular functions, and cellular locations.</li><li><strong>Chemicals-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">Chemical-pathway links</a> that were used to map 8,886 MESH concepts to 711,043 Reactome pathways.</li><li><strong>Chemicals-Genes:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">Chemical-Gene links</a> that were used to map 8,881 MESH concepts 410,379 Entrez genes.</li><li><strong>Chemicals-Diseases:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">Chemical-Disease links</a> that were used to map 14,238 MESH concepts 1,216,900 DOID concepts.</li><li><strong>Genes-Genes:</strong> The<a href="https://string-db.org/">STRING Database</a> provides <a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">Gene-Gene links</a> that were used to create 594,100 gene-gene interactions. When generating these mappings, only the inferred protein-protein relationships considered to be high confidence were used (score of 700 or better).</li><li><strong>Genes-Disease:</strong> Mappings between genes and diseases were retrieved from <a href="http://www.disgenet.org/web/DisGeNET/menu">DisGeNet</a> via SPARQL endpoint and used to map 6,051 Entrez genes to 20,452 DOID concepts.</li><li><strong>Genes-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">Gene-Pathway links</a> that were used to map 110,370 Entrez genes to 107,029 Reactome pathways.</li><li><strong>Pathways-Disease:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">Pathway-Disease links</a> that were used to map 1,818 Reactome pathways to 106,727 DOID concepts.</li></ul><p> </p><p><strong>Knowledge Graph</strong><br>The knowledge graph represented above was built using the following steps: Merge Ontologies: Merge ontologies using the <a href="https://github.com/owlcollab/owltools/wiki">OWL Tools API</a><br>Express New Ontology Concept Annotations: Create new ontology annotations by asserting a relation between the instance and an instance of the ontology class. For example to assert the following relations:</p><blockquote><p><a href="https://www.ncbi.nlm.nih.gov/mesh/68009020">Morphine</a> --> <a href="https://www.ebi.ac.uk/ols/ontologies/ro/properties?iri=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_0002606">is substance that treats</a> --> <a href="https://hpo.jax.org/app/browse/term/HP:0002076">Migraine</a></p><p>We would need to create two axioms:</p><ul><li>isSubstanceThatTreats(Morphine, x1)</li><li>instanceOf(x1, Migraine)</li></ul></blockquote><p>While the instance of the HP class hemiplegic migraines can be treated as an anonymous node in the knowledge graph, we generate a new international resource identifier for each newly generated instance.</p><p><strong>Deductively Close Knowledge Graph:</strong> The knowledge graph is deductively closed by using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies).</p><p><strong>Generate Edge List:</strong> The final step before exporting the edge list is to remove any nodes that are not biologically meaningful or would otherwise reduce the performance of machine learning algorithms and the algorithm used to generate embeddings.</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-3,-2019">here</a>.</p>
RaNDT Radar Benchmark
<p>© 2023. This work is licensed under a CC BY-NC-SA 4.0 license by INSTITUTE OF MECHANISM THEORY, MACHINE DYNAMICS AND ROBOTICS - RWTH AACHEN UNIVERSITY.</p> <p>The data set contains multimodal sensor data generated by a tracked mobile robot in an outdoor and an indoor environemnt. Sensors include radar (indurad iSDR-C), LiDAR (SICK TiM), and IMU (Phidgets IMU). The data is used for benchmarking the RaNDT SLAM available at <a href="https://github.com/IGMR-RWTH/RaNDT-SLAM">IGMR Github</a>.</p> <p>The data is related to a publication accepted at IEEE IROS 2024. The pre-print is available at <a href="https://www.arxiv.org/abs/2408.11576" target="_blank" rel="noopener">arxiv</a>.<br><br></p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
FIND: A Function Interpretation Dataset and Benchmark for Evaluating Interpretability Methods
<p><strong>FIND</strong> is an interactive dataset for evaluating AI interpretability methods on black box functions. </p> <p>This dataset contains all function files for the <strong>FIND</strong> benchmark and JSON files with associated metadata. The utilities provided in the associated <strong>FIND</strong> <a href="https://github.com/multimodal-interpretability/FIND">GitHub Repository</a> support running and evaluating interpretation of the functions with user-defined interpreters.</p>
SPEChpc 2021 Benchmarks: A Performance and Energy Case Study
SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids based Infiniband Clusters: A Performance and Energy Case Study.
Benchmark movement data set for trust assessment in human robot collaboration
<p>In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016).</p> <p>Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants, 7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens:</p> <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> <p>The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are:</p> <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> <p>This is followed by the trust questionaire items:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>The last two columns are</p> <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>
GE Discovery TOF MI PET NEMA IQ projector benchmark listmode data
<p>## LIST0000.BLF</p> <p>listmode file from GE Discvoery MI PET/CT containing all acquired emission events (HDF5)<br> of a single bed position NEMA IQ phantom acq.</p> <p>## corrections.h5</p> <p>file containing all quantitative corrections estimate using GE's duetto tool box (HDF5)</p> <p>- correction_lists/sens -> sensivity value for acquired events<br> - correction_lists/atten -> attenuation value for acquired events<br> - correction_lists/contam -> additive contaminations (randoms + scatter) for all acquired events<br> - all_xtals/atten -> attenuation values for all possible crystal combinations<br> - all_xtals/sens -> sensitivity values for all possible crystal combinations<br> - all_xtals/xtal_ids -> all possible crystal combinations</p> <p> </p>
BLM-AgrF: A New French Benchmark to Investigate Generalization of Agreement in Neural Networks
<p>BLM-AgrF is a French dataset for learning the underlying rules of subject-verb agreement in sentences, developed in the BLM framework, a new task inspired by visual IQ tests known as Raven's Progressive Matrices. In this task, an instance consists of sequences of sentences with specific attributes. To predict the correct answer as the next element of the sequence, a model must correctly detect the generative model used to produce the dataset.</p>
Hubbard Brook Experimental Forest Benchmarks: GIS Shapefile
This coverage was obtained in digital form from Chris Barton of the USGS. Bedrock geology in the Hubbard Brook Valley was mapped by C.C. Barton, R.H. Comerlo, and S.W. Bailey, August 1994 to August 1995. The Map is entitled "BEDROCK GEOLOGIC MAP OF HUBBARD BROOK EXPERIMENTAL FOREST AND MAPS OF FRACTURES AND GEOLOGY IN ROADCUTS ALONG INTERSTATE 93, GRAFTON COUNTY, NEW HAMPSHIRE" and was approved for publication on August 28, 1995. The benchmark adjacent to the Hubbard Brook Headquarters building was set by the New Hampshire Department of Transportation in 1993. The station is a standard NHDOT disk stamped "Hubbard Brook 1993", set into the top of a 4 FT long by 5 IN granite monument flush with the ground and level with the parking lot. Located 17.5 FT (5.3 M) southeast from the southeast corner post for a fence and the orange carsonite marker, 49.5 FT (15.1 M) northeast from the north corner of the office building, 60 FT (18.3 M) east from the west face of the concrete curb, 51.0 FT (15.5 M) south from the northeast corner post for a fence. Benchmark location: NAD83 Latitude = 43 56 38.0361 Longitude = 71 42 03.9897 Northing = 160408.164 Meters Easting = 297235.062 Meters NAD27 Latitude = 43 56 37.7891 Longitude = 71 42 05.7012 Northing = 526,241.45 Feet (State Plane Zone 4676) Easting = 490,803.21 Feet Elevation = 833.87 Feet NGVD29 (El order 31). Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
Benchmarks for Evaluation and Comparison of Three-way Model Merging Techniques
<p>The attached files are intended to allow the interaction of researchers in the field of Model Merging Conflict Detection and Resolution. For people who want to add the results of evaluating a new technique and contribute to the creation of the actual body of knowledge, it is necessary to download the raw files and fill them out.</p>
European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data
<p>The files uploaded under this doi number represent the updated data sets used in the manuscript submitted to ESSDD in its revised version entitled: "European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data" Excel files including the data behind all the manuscript figures are available for download only for review purposes. We added, as sugggested by the referees, metadata belonging to UNFCCC 2018, FAOSTAT, EDGAR v4.3.2, CAPRI and CBM.</p>
Dataset related to the article "Laboratory-scale hydraulic fracturing dataset for benchmarking of Enhanced Geothermal System simulation tools"
<p>Experimental results from hydraulic fracturing experiments performed in granite and marble samples of size 30 cm × 30 cm × 45 cm under well-defined boundary conditions.</p> <p>Datasets include:</p> <ul> <li>pressure versus flow-rate response</li> <li>acoustic emission data from a dense network of 32 seismic sensors</li> <li>detailed description of the experimental set-up and adopted test protocol</li> <li>mechanical and petrophysical properties of the samples</li> <li>python code for seismic data processing</li> </ul> <p>This complete collection of data, obtained within the framework of European Union’s Horizon 2020 project GEMex, is rare in its kind and indispensable for verification of model assumptions and constitutive relationships of numerical codes used for designing field-scale hydraulic fracturing experiments.</p>
Benchmark set for relative free energy calculations
<p>Created by Christina Schindler and Daniel Kuhn, Merck KGaA, Darmstadt, Germany.</p> <p>December 2018</p> <p>Manuscript in preparation.</p> <p>Previously presented at Alchemical Free Energy Workshop 2019 in Goettingen, Germany.</p> <p>DOI: 10.5281/zenodo.3258925</p> <p> </p> <p>References for datasets used in benchmark</p> <p>CDK8<br> Schiemann, Kai, et al. "Discovery of potent and selective CDK8 inhibitors from an HSP90 pharmacophore." Bioorganic & medicinal chemistry letters 26.5 (2016): 1443-1451.</p> <p>DOI: 10.1016/j.bmcl.2016.01.062</p> <p>c-Met</p> <p>Dorsch, Dieter, et al. "Identification and optimization of pyridazinones as potent and selective c-Met kinase inhibitors." Bioorganic & medicinal chemistry letters 25.7 (2015): 1597-1602.</p> <p>DOI: 10.1016/j.bmcl.2015.02.002<br> Eg5</p> <p>Schiemann, Kai, et al. "The discovery and optimization of hexahydro-2H-pyrano [3, 2-c] quinolines (HHPQs) as potent and selective inhibitors of the mitotic kinesin-5." Bioorganic & medicinal chemistry letters 20.5 (2010): 1491-1495.</p> <p>DOI: 10.1016/j.bmcl.2010.01.110<br> Hif2a</p> <p>Wallace, Eli M., et al. "A small-molecule antagonist of HIF2α is efficacious in preclinical models of renal cell carcinoma." Cancer research 76.18 (2016): 5491-5500.</p> <p>DOI: 10.1158/0008-5472.CAN-16-0473</p> <p>Dixon, Darryl David, et al. "Aryl ethers and uses thereof." U.S. Patent No. 9,908,845. 6 Mar. 2018.</p> <p>URL: Google Patents<br> PFKFB3</p> <p>Boutard, Nicolas, et al. "Discovery and Structure–Activity Relationships of N-Aryl 6-Aminoquinoxalines as Potent PFKFB3 Kinase Inhibitors." ChemMedChem 14.1 (2019): 169-181.</p> <p>DOI: 10.1002/cmdc.201800569<br> SHP2</p> <p>Chen, Ying-Nan P., et al. "Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases." Nature 535.7610 (2016): 148.</p> <p>DOI:10.1038/nature18621</p> <p>Garcia Fortanet, Jorge, et al. "Allosteric inhibition of SHP2: identification of a potent, selective, and orally efficacious phosphatase inhibitor." Journal of medicinal chemistry 59.17 (2016): 7773-7782.</p> <p>DOI: 10.1021/acs.jmedchem.6b00680</p> <p>Chen, Christine Hiu-tung, et al. "1-pyridazin-/triazin-3-yl-piper (-azine)/idine/pyrolidine derivatives and compositions thereof for inhibiting the activity of shp2." U.S. Patent Application No. 15/110,498.</p> <p>URL: Google Patents</p> <p> </p> <p>SYK<br> Currie, Kevin S., et al. "Discovery of GS-9973, a selective and orally efficacious inhibitor of spleen tyrosine kinase." Journal of medicinal chemistry 57.9 (2014): 3856-3873.</p> <p>DOI: 10.1021/jm500228a</p> <p>TNKS2<br> Buchstaller, Hans-Peter, et al. "Discovery and Optimization of 2-Arylquinazolin-4-ones into a Potent and Selective Tankyrase Inhibitor Modulating Wnt Pathway Activity." Journal of medicinal chemistry 62.17 (2019): 7897-7909.</p> <p>DOI: 10.1021/acs.jmedchem.9b00656</p>
Replication package of A benchmark-based evaluation of search-based crash reproduction
<p>Release of the reproduction package of Soltani, M., Derakhshanfar, P., Devroey, X. and van Deursen, A. (2020). A benchmark-based evaluation of search-based crash reproduction. In Empirical Software Engineering. 25, 1 (Jan. 2020), pp. 96–138.</p>
anTraX: high throughput video tracking of color-tagged insects (benchmark datasets)
<p>Datasets used to benchmark anTraX tracking software. Each dataset contains the raw videos, a configured anTraX session with all parameters required to reproduce the tracking results from the paper, as well as the tracking output for the first video in each dataset.</p> <p> </p> <p> </p>
Locally Injective Mappings Benchmark
<p>We are glad to release the benchmark dataset of 2D/3D meshes in our Siggraph 2020 paper <a href="https://duxingyi-charles.github.io/publication/lifting-simplices-to-find-injectivity/">Lifting Simplices to Find Injectivity</a> The dataset collects challenging examples from recent papers on fixed boundary injective mappings. It also includes hundreds of newly created examples. The dataset includes <em>10743</em> triangular mesh examples and <em>904</em> tetrahedral mesh examples. The examples are divided into 3 categories, 2D parameterization, 3D parameterization and 3D deformation.</p> <p>We hope that our dataset offers a benchmark for future research in this area.</p> <p>A more detailed introduction to the dataset can be find <a href="https://github.com/duxingyi-charles/Locally-Injective-Mappings-Benchmark">here</a>.</p>
SPEC CPU 2017 Benchmark Suite Results for the HPE ProLiant DL385 Gen10 server
<p>Benchmark results for the SPEC CPU 2017 benchmark suite with the full power and temperature data. Data is collected on a state-of-the-art HPE ProLiant DL385 Gen10 server. Results contain two full runs of the SPEC CPU 2017 benchmark suite. One result set of all four benchmark suites with compiler optimizations matching official results and a second set with moderate, real-world, compiler settings.</p>
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack: Experimental Data
<p>Full experimental dataset for the publication "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack". The archive `application_motivated_benchmarks.zip` contains the following files and directories:</p> <p>- uncompiled_log.csv</p> <p>Gives IDs for the uncompiled circuits initially generated for use in our<br> experiments, along with the properties of the circuits.</p> <p>- properties_log.csv</p> <p>Gives IDs for device property files, along with the device and the time at which<br> they were collected.</p> <p>- compiled_log.csv</p> <p>Gives the calculated figures of merits for the compiled and run circuits.<br> Compiled circuits are identified by the ID of the uncompiled circuit, the<br> compilation strategy used, and the device compiled onto. Device property IDs at<br> the time of compilation and run are given.</p> <p>- circuits/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 2 forms.<br> <br> - uncompiled.qasm is the uncompiled circuit.<br> - files of the form 'strategy'_'device'.qasm are the compiled circuits.</p> <p>- data/</p> <p>Contains a subdirectory for each uncompiled circuit. Each subdirectory has files<br> of 3 forms.</p> <p> - prob_vector.csv contains the ideal output probability distribution.<br> - files of the form 'strategy'_'device'.csv contain the shot counts for<br> each compiled circuit when run on the real device.<br> - files of the form 'strategy'_'device'_simulated.csv contain the shot<br> counts for each compiled circuit when run using a classical simulator<br> with noise model build from device properties at the time of the real<br> run.</p> <p>- device_properties/</p> <p>Contains json files detailing device properties for each device property ID.</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.